{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d90cbc83",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "#from vk_download import main\n",
    "import scipy.sparse as sprs\n",
    "import time\n",
    "from tqdm.auto import tqdm\n",
    "import os\n",
    "import time\n",
    "import pandas as pd\n",
    "import random\n",
    "import datetime\n",
    "from tqdm.auto import tqdm\n",
    "from threading import Thread\n",
    "from multiprocessing import Process\n",
    "import numpy as np\n",
    "from scipy.sparse import csr_matrix, lil_matrix\n",
    "import scipy.sparse as sprs\n",
    "import os\n",
    "from collections import OrderedDict\n",
    "import shutil\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import networkx as nx\n",
    "from IPython.display import clear_output\n",
    "from statsmodels.stats.proportion import proportion_confint\n",
    "from scipy import stats\n",
    "import statsmodels.api as sm\n",
    "from scipy.optimize import minimize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5299f5ec-41d7-4dc6-8d5a-204f592c35c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def LikelihoodFunction(theta, Distr, N_Succ, N_Obs):\n",
    "    \n",
    "    theta = np.array(theta)  # vector of parameters\n",
    "\n",
    "    ProbNullModel = 1 / N_Obs  # the null model\n",
    "    \n",
    "    Vector = np.zeros(N_Succ)  # components of this vector will be tie creation probabilities of successfull observations\n",
    "    \n",
    "    for i in range(len(theta)):\n",
    "        \n",
    "        Vector = Vector + theta[i]*Distr[i]\n",
    "        \n",
    "    Vector = Vector + (1 - theta.sum())*ProbNullModel\n",
    "    \n",
    "    L = np.log(Vector).sum()\n",
    "    \n",
    "    return L \n",
    "\n",
    "\n",
    "\n",
    "def ObjFunction(theta, Distr, N_Succ, N_Obs):\n",
    "    \n",
    "    return - LikelihoodFunction(theta, Distr, N_Succ, N_Obs)  # we will minimize this quantity\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0be3d60a-4ccb-4f4c-b319-3d88d9eab484",
   "metadata": {},
   "source": [
    "# Choose city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5c728b84-cb88-47e1-b587-0b475744cf40",
   "metadata": {},
   "outputs": [],
   "source": [
    "#folder = 'MainCity'  # This city was used in analysis\n",
    "\n",
    "\n",
    "# These two cities were used to check robustness of our results\n",
    "\n",
    "#folder = 'City(1)'  # Robustness 1\n",
    "#folder = 'City(2)'  # Robustness 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f595ff1-8743-439d-9f55-93101f4553ec",
   "metadata": {},
   "source": [
    "# Load data and prepare distributions in accordance with the mechanisms presetned in Table 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4a92186a-f9f1-42fa-b62c-078f56c6160d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of observations (connected pairs): 172827\n",
      "\n",
      "Number of successes (connected pairs that have become disjointed): 3127\n"
     ]
    }
   ],
   "source": [
    "Target = np.load(f'{folder}/Masks/Target.npy')\n",
    "\n",
    "\n",
    "MaskDeleteTie = (Target == 3)\n",
    "MaskWereFriends = (Target == 3) | (Target == 4)\n",
    "\n",
    "MaskStaticOpinions = np.load(f'{folder}/Masks/MaskStaticOpinions.npy')\n",
    "\n",
    "FriendsBeforeNumI = np.load(f'{folder}/Vectors/FriendsBeforeNumI.npy')\n",
    "FriendsBeforeNumJ = np.load(f'{folder}/Vectors/FriendsBeforeNumJ.npy')\n",
    "\n",
    "CommFriendsBefore = np.load(f'{folder}/Vectors/CommFriendsBefore.npy')\n",
    "\n",
    "OpinionBeforeI = np.load(f'{folder}/Vectors/OpinionBeforeI.npy')\n",
    "OpinionBeforeJ = np.load(f'{folder}/Vectors/OpinionBeforeJ.npy')\n",
    "\n",
    "ageI = np.load(f'{folder}/Vectors/ageI.npy')\n",
    "ageJ = np.load(f'{folder}/Vectors/ageJ.npy')\n",
    "\n",
    "sexI = np.load(f'{folder}/Vectors/sexI.npy')\n",
    "sexJ = np.load(f'{folder}/Vectors/sexJ.npy')\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "FriendsBeforeNumI = FriendsBeforeNumI[MaskWereFriends*MaskStaticOpinions]\n",
    "FriendsBeforeNumJ = FriendsBeforeNumJ[MaskWereFriends*MaskStaticOpinions]\n",
    "\n",
    "MaskDeleteTie = MaskDeleteTie[MaskWereFriends*MaskStaticOpinions]\n",
    "\n",
    "CommFriendsBefore = CommFriendsBefore[MaskWereFriends*MaskStaticOpinions]\n",
    "\n",
    "OpinionBeforeI = OpinionBeforeI[MaskWereFriends*MaskStaticOpinions]\n",
    "OpinionBeforeJ = OpinionBeforeJ[MaskWereFriends*MaskStaticOpinions]\n",
    "\n",
    "ageI = ageI[MaskWereFriends*MaskStaticOpinions]\n",
    "ageJ = ageJ[MaskWereFriends*MaskStaticOpinions]\n",
    "\n",
    "sexI = sexI[MaskWereFriends*MaskStaticOpinions]\n",
    "sexJ = sexJ[MaskWereFriends*MaskStaticOpinions]\n",
    "\n",
    "del(MaskWereFriends, MaskStaticOpinions)\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "FriendsBeforeProduct = FriendsBeforeNumI * FriendsBeforeNumJ\n",
    "\n",
    "del(FriendsBeforeNumI, FriendsBeforeNumJ,)\n",
    "\n",
    "PA_Distr = FriendsBeforeProduct.max() - FriendsBeforeProduct + 1\n",
    "\n",
    "PA_Distr = PA_Distr / PA_Distr.sum()\n",
    "\n",
    "del(FriendsBeforeProduct)\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "TransitivityDistr = CommFriendsBefore.max() - CommFriendsBefore + 0.1\n",
    "\n",
    "TransitivityDistr = TransitivityDistr / TransitivityDistr.sum()\n",
    "\n",
    "del(CommFriendsBefore)\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "OpinionDiffBefore = abs(OpinionBeforeI - OpinionBeforeJ)\n",
    "\n",
    "OpinionSelectivityDistr = OpinionDiffBefore + 0.01\n",
    "\n",
    "del(OpinionBeforeI, OpinionBeforeJ, OpinionDiffBefore)\n",
    "\n",
    "OpinionSelectivityDistr = OpinionSelectivityDistr / OpinionSelectivityDistr.sum()\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "ageDiff = abs(ageI - ageJ)\n",
    "\n",
    "AgeSelectivityDistr = ageDiff + 1\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "AgeProduct = ageI * ageJ\n",
    "\n",
    "ActivityDistr = AgeProduct.max() - AgeProduct + 1\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "del(ageI, ageJ, ageDiff)\n",
    "\n",
    "ActivityDistr = ActivityDistr / ActivityDistr.sum()\n",
    "\n",
    "AgeSelectivityDistr = AgeSelectivityDistr / AgeSelectivityDistr.sum()\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "sexDiff = abs(sexI - sexJ)\n",
    "\n",
    "GenderSelectivityDistr = sexDiff + 0.1\n",
    "\n",
    "del(sexI, sexJ, sexDiff)\n",
    "\n",
    "GenderSelectivityDistr = GenderSelectivityDistr / GenderSelectivityDistr.sum()\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "PA_DistrSucc = PA_Distr[MaskDeleteTie]\n",
    "TransitivityDistrSucc = TransitivityDistr[MaskDeleteTie]\n",
    "OpinionSelectivityDistrSucc = OpinionSelectivityDistr[MaskDeleteTie]\n",
    "AgeSelectivityDistrSucc = AgeSelectivityDistr[MaskDeleteTie]\n",
    "GenderSelectivityDistrSucc = GenderSelectivityDistr[MaskDeleteTie]\n",
    "\n",
    "ActivityDistrSucc = ActivityDistr[MaskDeleteTie]\n",
    "\n",
    "N_ConnectedPairs = len(PA_Distr)\n",
    "N_DeletedTies = len(PA_DistrSucc)\n",
    "\n",
    "del(TransitivityDistr, \n",
    "    PA_Distr, \n",
    "    MaskDeleteTie, OpinionSelectivityDistr, AgeSelectivityDistr, GenderSelectivityDistr, ActivityDistr)\n",
    "\n",
    "print(f'Number of observations (connected pairs): {N_ConnectedPairs}')\n",
    "\n",
    "print('')\n",
    "\n",
    "print(f'Number of successes (connected pairs that have become disjointed): {N_DeletedTies}')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d0d62a6-c66d-4e14-8b64-0ab19f81d217",
   "metadata": {},
   "source": [
    "# Glossary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ba5a37bf-1b19-4c35-97b8-9f825bc72103",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# PA_DistrSucc                   -   preferential attachment distribution\n",
    "\n",
    "# TransitivityDistrSucc          -   transitivity distribution\n",
    "\n",
    "# OpinionSelectivityDistrSucc    -   opinion selectivity distribution\n",
    "\n",
    "# AgeSelectivityDistrSucc        -   age selectivity distribution\n",
    "\n",
    "# GenderSelectivityDistrSucc     -   gender selectivity distribution\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf5fce68-db0c-4ceb-8ab9-3bb52fb308bb",
   "metadata": {},
   "source": [
    "# Plot likelihood functions (single variate analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "52c1dcef-32e5-4068-903a-237fd2e07b9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# PA mechanims\n",
    "\n",
    "resPA = []\n",
    "Distr = [PA_DistrSucc]\n",
    "\n",
    "ThetaGrid = np.arange(0, 1.05, 0.05)\n",
    "\n",
    "for theta in ThetaGrid:\n",
    "    \n",
    "    Likelihood = LikelihoodFunction([theta], Distr, N_DeletedTies, N_ConnectedPairs)\n",
    "    \n",
    "    resPA.append(Likelihood)\n",
    "    \n",
    "# Triadic closure\n",
    "\n",
    "resTransitivity = []\n",
    "Distr = [TransitivityDistrSucc]\n",
    "\n",
    "ThetaGrid = np.arange(0, 1.05, 0.05)\n",
    "\n",
    "for theta in ThetaGrid:\n",
    "    \n",
    "    Likelihood = LikelihoodFunction([theta], Distr, N_DeletedTies, N_ConnectedPairs)\n",
    "    \n",
    "    resTransitivity.append(Likelihood)\n",
    "        \n",
    "# Opinion selectivity\n",
    "\n",
    "resOpinionSelectivity = []\n",
    "Distr = [OpinionSelectivityDistrSucc]\n",
    "\n",
    "ThetaGrid = np.arange(0, 1.05, 0.05)\n",
    "\n",
    "for theta in ThetaGrid:\n",
    "    \n",
    "    Likelihood = LikelihoodFunction([theta], Distr, N_DeletedTies, N_ConnectedPairs)\n",
    "    \n",
    "    resOpinionSelectivity.append(Likelihood)\n",
    "    \n",
    "# Age selectivity\n",
    "\n",
    "resAgeSelectivity = []\n",
    "Distr = [AgeSelectivityDistrSucc]\n",
    "\n",
    "ThetaGrid = np.arange(0, 1.05, 0.05)\n",
    "\n",
    "for theta in ThetaGrid:\n",
    "    \n",
    "    Likelihood = LikelihoodFunction([theta], Distr, N_DeletedTies, N_ConnectedPairs)\n",
    "    \n",
    "    resAgeSelectivity.append(Likelihood)\n",
    "    \n",
    "# Gender selectivity\n",
    "\n",
    "resGenderSelectivity = []\n",
    "Distr = [GenderSelectivityDistrSucc]\n",
    "\n",
    "ThetaGrid = np.arange(0, 1.05, 0.05)\n",
    "\n",
    "for theta in ThetaGrid:\n",
    "    \n",
    "    Likelihood = LikelihoodFunction([theta], Distr, N_DeletedTies, N_ConnectedPairs)\n",
    "    \n",
    "    resGenderSelectivity.append(Likelihood)\n",
    "    \n",
    "# Age-based activity\n",
    "\n",
    "resAgeActivity = []\n",
    "Distr = [ActivityDistrSucc]\n",
    "\n",
    "ThetaGrid = np.arange(0, 1.05, 0.05)\n",
    "\n",
    "for theta in ThetaGrid:\n",
    "    \n",
    "    Likelihood = LikelihoodFunction([theta], Distr, N_DeletedTies, N_ConnectedPairs)\n",
    "    \n",
    "    resAgeActivity.append(Likelihood)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "566649e1-2462-4558-98fb-ee07b8192d2d",
   "metadata": {},
   "source": [
    "# Plot Figure 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a1e01239-502f-4d46-a57b-2c4f6159f919",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zBw8eMGvWLNq2bYuHhwc2NjZ4eXlRv359/Pz8WLRoEVFRUWbLp6Sk8O2339K9e3cqVKiAra0t5cqVw9fXl19++aVA9lm9c+cOH3zwAS1atMDT0xM7OzuqVq3K8OHD2b17t0V1REdH8+mnn9K+fXvKlSuHjY0NHh4etG7dmunTp3PmzBn12vHjx6PRaAgKCgIgKCjIKNarUaOGQf01atRAo9Ewfvx4g+PdunVDo9FQtWrVXLcoSklJwc3NDY1Gw/Dhww3OmdsjVH9fvZkzZxq1Vd+mwYMHo9Fo8PDwUP8/MScjI4MKFSqg0Wjo06dPjtcKIYQQhcHBwYFff/1V/T03Z86cIpmQk5qaqvbJDB8+HBsbG7Wf8syZM5w+fdqiepKSkvjss8949tln1dixQ4cOLFmyBEVRzPZrPepxYkdLpKWl4e/vz5QpU2jZsqUaD5YpU4bWrVszY8YM7t27Z1FdqampLF68mL59+1K5cmXs7OxwcnKiQYMGvPTSS+zatUuNh5YtW4ZGo2HmzJlq+UdjmEdjW32M9mgc9umnn6rXX7p0Kdd29urVC41GQ8WKFcnMzFSPm9s/Pi+x4TfffKMeO3LkSK5tGTJkCBqNBk9Pz1zjMyFKDUUIIZ5C+/btUwAFUD755JMiv//x48fV+69evVpRFEXR6XRKzZo1FUDx8/PLtY64uDilTZs2aj2PfrRq1Uo5deqU+vXSpUtN1nP//n2lW7duZusBlHLlyimHDx/O9+udN2+eotVqc7wHoDx8+NBk+dmzZys2NjZmy2k0GuWjjz4yWfbatWu5vgfJycnKCy+8kGPbnJyclK1bt5p9jUlJScqIESNyfY3Zv99yu/bR67N/3+7bt8/g/h06dFAApWbNmmbbqNe3b18FULy8vJT09PRc36vs983pQ9+mYcOGKYDi7u6uJCcn59gWPz8/BVA8PT2VlJSUXNsuhBDi6aDT6ZQyZcoogOLi4qIkJCQUd5MMNG3aVI1Brl+/XqB17969W33t5j769OljNm7KHkN89913irW1tck6HB0dlaCgILPtuH37tlKvXj2zbejVq5eya9cus7GJXnh4uNK4ceMcX4+3t7cSFhZmsnznzp0VQOncubOyefNmxcnJyaj8tWvXLH5/z58/r1SqVCnXuGbhwoUmy58+fVqpXr16jmVbtmyp3LlzJ9fXY87KlStNvs7sHxMnTjSI4/JTR/Xq1dXrx40bl+t7kv16RVHU92HcuHEGx3/++We1zP79+822UVEUZf369eq1mzdvtui9yu39z96mgIAA9diaNWtybMuWLVvUa9evX5/jtUIIIYQl8tv/2LNnT7XcoUOHCq+B/5P993FISIiiKIqSkJCgxhJTp07NtY7IyEilTp06Zn839+vXTwkMDCz02NESlsQ9ZcqUUQ4ePJhjPadOnVL7cnP60MeqS5cuzfXaR2NbfVsfjcMuXbqkXj9jxowc23nnzh3FyspKAZQ333zT4Nwnn3yi1pPX90jfppiYGMXOzk4BlH/96185tiU6Olrt43311VdzvFaI0kT2bBdCiGKgX5rJxcWFgQMHAlkjGUeOHMmsWbMICAjg/v37eHp6mq3jhRdeUEcLtm/fntdeew1vb2+io6NZuXIlq1atynVmcWpqKr6+vpw8eRIrKytGjhxJnz59qFmzJunp6Rw4cIB58+Zx9+5d+vTpw6lTp6hevXqeXuuZM2d4++230el01KxZkylTptCkSRM8PT15+PAhYWFh7Nu3jy1btpgsP3v2bKZPnw7As88+y+TJk6lTpw7u7u6EhYXx7bffcvjwYT777DPKli3L66+/nqf2KYrC0KFD1dUE+vfvz/Dhw6lVqxZarZZjx44xd+5cIiIiGDp0KIcOHaJFixYGdeh0OgYOHMgff/wBQJ06dfj3v/9NixYtcHR05Pbt24SEhBjN3P7rr7+4desWvXr1AuDzzz9Xvx/0ypUrZ9HrGDVqFAcPHuTatWuEhISYnWEXExNDYGAgkDVa2do691CgZcuW/PXXX2zZsoUPP/wQgF27dlGpUiWD6/Sz9F966SXWr1/PgwcP2LRpEyNGjDBZb3R0NNu2bVPbb2dnZ9FrFUII8eQ7e/YsMTExAHTs2BEnJ6dibtE/4uLi+PPPPwGoV69enmOjnBw6dIjnnnuO9PR0ypcvz2uvvUbjxo2pVKkSt27dYt26daxcuZLt27czbtw4Nm7caLauXbt2cezYMRo1asQbb7xBo0aNSE5OZtOmTSxYsICkpCTGjBnDpUuXjPYCzcjIoF+/furs9549ezJ58mSqVq1KREQE33//Pbt27eL+/fs5vp6YmBg6dOhAZGQkdnZ2vPzyy3Tu3JkaNWqQkJBAYGAgCxYs4PLlyzz33HOcPHkSNzc3k3VFREQwevRoHB0d+eijj+jYsSNWVlYcP34cZ2dni9/jMWPGcOvWLWxsbHj55Zd57rnnqFChAjqdjhs3bnDkyBE2bdpksuzly5fp3LkzcXFxuLq68uqrr9KqVSuqVq1KTEwMW7du5ccff+T48eMMHDiQ4OBgbGxsLG4bwG+//caYMWNQFIVatWoxZcoU6tevj5eXF9evX+eXX35h+/bt/PLLL7i6ujJv3jyjOn799VfGjh0LZK2kkP11JiQkcObMGbZu3WowC2rWrFm8/fbbTJgwgdDQUFq0aMHSpUsN6rV0z9ghQ4bw6quvkpqayqpVq+jcubPZa1evXg2Ah4cHzz33nEX1BwYGkpaWRqNGjQCYPHky//73vw2u8fDwAKB3795UqVKFGzdusHTpUl544QWz9epfb9myZRkwYIBFbRFCCCEKg6+vr9p3ExwcnKdVlPJD309Zu3Zt2rZtC4CTkxODBg1i1apVrF69mtmzZ2NlZWWyfHp6On379lVji759+/Lyyy+rv4MXL17Mtm3biI6OzrEdBRk75iQjI4NatWrh5+dHq1atqFatGtbW1oSHh7N7926WLFlCTEwMfn5+nD171mTf3IULF+jYsSMJCQkA+Pn58cILL1CrVi0yMzO5ePEigYGBBnHloEGDaNGiBd9//z2LFi0CsvoGH1W5cuVcX4O3tzetW7fm6NGjrF69mk8++cTstevWrVNns48aNSrXuiFvsaGnpyeDBw9mzZo1rF27lvnz5+Pg4GCy3lWrVqmrNbz44osWtUWIUqG4s/1CCFEcso8snTx5svLXX3+Z/YiKiirQe6elpSleXl4KoIwdO9bg3IULF9R2fffdd2br2Lx5s3rd4MGDlczMTKNr5syZYzDa0NSs7vfff1+BrBnIoaGhJu91/fp1pWLFigqgjBw5Mm8vVlGUjz76SIGsmeHmZvgoiqI8ePDA6HWcO3dOHe34ySefKDqdzqhcZmamMnr0aAVQnJ2dlfv37xucz21m++LFixVAsbGxUXbs2GGybffv31caNGigAEr79u2Nzi9YsEC9h5+fn9kZ2pmZmcqNGzfy1L7scprZfu/ePYtGhi5atMhotLKlbck+AjenGWSZmZnqjKMePXqYvW7evHlqfadOnTJ7nRBCiKfPypUr1d8RH374YXE3x8DBgwfVto0ePbrA6k1LS1Nq1KihAErv3r2VxMREk9fpYxdACQwMNDqfPf7r06ePkpqaanTN559/rl7z+++/G53/9ttv1fOvvPKKyXa8+OKLBvcyNTtp5MiR6qyXq1evmqzn5MmT6qyp999/3+i8fnYzoFSqVEkJDw83WY8lrly5otZlbua6omStrPBoTKkoitKuXTsFUJo2bapER0ebLLtjxw51RafFixebfT2mZrZHR0crbm5uCqC8+OKLZmeu62N4rVar/P333wbnbt26pTg6OiqQtTrVX3/9ZfZ1RkRE5Kl9jzI3s11RFGXw4MEKoHh4eJj8HlSUrPhfPwvK1PdZbm3RP8vcZgrq/x7RarVKZGSkyWuioqLUWPqNN97IsT4hhBDCUvmd2b5792613Isvvlh4DVQU5e7du+rvwI8//tjg3I4dO9R2BAQEmK3j66+/Vq97dOa03pQpU4okdrTE5cuXTfYx6p05c0ZxdnbO8W+RZs2aqfFFTqvn3Lt3T0lKSjI4Zm42uSnmZrYriqJ88803aj3Hjx83W0fr1q0VQPHx8TE6l1tbLI0N9+zZo9azatUqs9fpVy1o3LhxjvUJUdrInu1CiKfeokWLaNSokdmP77//vkDvt2PHDnUk5+jRow3OPfPMM+qsaf2oUlN++OEHIGsvpx9++AGt1vi/82nTptGsWTOzdSQkJPDdd98B8Nlnn9G8eXOT11WvXp2PPvoIgPXr15OYmGi2TlPu3LkDgI+PD+XLlzd7nZubm9HrmDt3Lunp6bRo0YJPPvnEaP8gAK1Wy8KFC7GzsyMhIYENGzZY3DZFUfjyyy8BeP311+ndu7fJ6zw8PJg9ezaQNeMs+ywgnU6nnqtSpQorVqwwO0Nbq9VaNDo1P8qUKaO2/7fffjO7V+iqVasAqFWrljpauaBptVp1dOqePXuIjIw0eZ1+RGzTpk1p0qRJobRFCCFE6ZR9j0QvLy+z1+l0Os6ePWv2ozD2uLS0bZA148Vc2x6NqdauXcv169ext7dnxYoVODo6mqzz5ZdfplWrVkDWvo/m2Nvbs3TpUpOzkV9//XX1eHBwsNF5ffxbvnx55s+fb7L+BQsW5Pj6r1+/zrp16wD49ttv1RVwHtW0aVNeffXVXF8PwH//+1+qVauW4zU50celAJ06dTJ7nX6P7+yCg4MJCQkBYPny5ZQtW9Zk2d69ezN06FAg99fzqEWLFhEXF0flypX5/vvvza5ANHPmTCpXroxOpzP6m2HhwoUkJSUBsHjxYho2bGj2flWrVs1T+/JCP2sqNjaWHTt2mLxmw4YNpKamGlxfGF588UU0Gg06nY7ly5ebvGblypUyy0oIIUSJUaZMGfXz2NjYQr3XmjVr1N+Bj/ZT9ujRgwoVKgCW9VNWqVKF//73vyav+eqrr4xWSMyuMGJHc2rXrm2yj1GvUaNGvPTSSwBs3rzZ6HxgYCAnT54EsuLqnFbOKVOmjNlZ3o/r+eefV1cb0Pf3PerKlSscPXoUKNx4q2vXrtSuXRvAaAa83smTJ9UVwiTeEk8aSbYLIUQR0wenFStWpHv37kbn9YHt0aNHDZK6ehkZGQQFBQFZnXnmOjk1Gg1jxowx246goCDi4uIA1A5Bc/Sdkenp6Zw4cSLHax9VsWJFAM6fP8+xY8fyVNbf3x/IWooypyDY3d1dXUby8OHDFtd//vx5rly5Alj+Hjx6j9OnT3Pjxg0gq/M7L8uYFjR90BwdHa0uaZ9dREQEhw4dAmDkyJGF2pYXX3wRrVZrtlPzxIkT6lJZEmALIYR41MOHD9XPc1pCPj4+PsdBkzdv3iy2tgG0bdvWbNuOHz9ucO3WrVsB6Ny5c65JfH1cklPc06NHD7Pb0bi4uFCnTh0Arl69anDu9u3bnD9/HsjacsZc0t/Z2Znhw4ebvX9AQACZmZk4Ojrmujy4/vXcunWLiIgIk9fY2toybNiwHOvJjT4uhbx3zuqfT926ddW40xz96zl+/LjZAZA53aNfv345bq9jbW2tDpp89HtAv0VPrVq1inUp9L59+6rLuuqXin+U/ni1atXo2LFjobWlRo0a+Pr6Auafu75TuHnz5jz77LOF1hYhhBDCEtn7lrLHnoVB30/ZqlUrNT7Us7KyUhPJW7duJT4+3qj8zZs3+fvvvwEYNmyY2RjGwcEhx1iuoGPHvIiNjeXKlSucO3dOHRjr7u4OZPUdPjqAVx9vAbz55puPff/8KleuHD169ACylorX6XRG12SPwwqzL1Cj0aj9e3v37jX5XPTxlq2tbaEm/oUoDpJsF0I89T755BMURTH7MWPGjAK7V2xsrJpAHjFihMkZ6SNGjFBHJZoaNXrlyhWSk5MBzM5G13t0b/HsQkND1c8rVqyIRqMx+5F9Rkz2GUGWGDFiBDY2NqSmptK+fXv69+/PDz/8wNmzZ1EUxWy58PBwdQWA9957L8f2aTQa9fXkpX3Z34O2bdvmWH/2P3Sy3+PUqVPq54XZSWiJAQMG4OLiApge0bpmzRr1PS/soLZKlSrqXvSmOjX1AbadnZ0E2EIIIYzof58BeV5Vp7AVVtv0ccmuXbtyjXvmzJkD5Bz3PPPMMznez9PTEzDuwM2+b2TLli1zrEM/wz6n15OUlIS1tXWOr6dfv35qOXOvqU6dOtjb2+fYntzUrFlTjdfmz59PgwYN+Pjjj9m7d686Gzy31xMWFpbr85kyZQqQNVA1t33t9TIzMzl9+jQAP/74Y6730K/mlP39Sk9P5+zZswB06NAhx8Gqhc3Ozk4dzOrv72/0fXbr1i32798PZP29UNht1c9Ou3z5stFqDqGhoer7JoNAhRBClATZf2+6uroW2n3OnTunTqp5dFa7nv54cnIy69evNzqv/x0KBdNPWVCxY27++usvXnzxRSpWrIinpyfe3t40bNhQHRir7w/W6XRGqwvo+wKrVatG9erV83X/gqLvU7t9+zZ79+41Oq9Ptrdu3Rpvb+9Cbcv48eOxsrIyOfEmNTVVbcvAgQMNVm8Q4kkgyXYhhChCa9euJS0tDTAfxJYrV46ePXsCWcsZPpqQzh7g5TbrKafzd+/etajNj8qtI/JRzzzzDGvWrMHDw4OMjAy2bdvG5MmTadSoEeXKlWPMmDEmly8tivYVxD2yLyWbfbZUcXBwcMDPzw/IWubq0fdCn4Bv1qxZrh3wBUHfqXnlyhUOHDigHs8eYA8aNMhomVYhhBAie+eLfvCdKe7u7kYDJceNG1ci2gbw4MEDg7Z98sknZq/NT1yiH4BpirkZ6Xr6QZ+ZmZkGx7Mnh83NjNfLaYuggo7lCipeWLNmjTor/Pz583z22Wd0794dd3d3OnXqxA8//EBKSopRucKOTe/fv5+nWfCm6r9//776t0Nxx6XwT+dvcnIyv//+u8G5tWvXqrOvimLg5aBBg9Sl/x9d2nTJkiVA1tYLhb36kxBCCGGJ7H1N+gGShUE/ycfa2trsUujNmzenXr16BtdnV9r6KQF++eUXmjVrxtKlSy1K1j8ac+ufT0mItwYNGqTG/Y9OvDl58qS66kBRxFuVKlWiT58+QNbEm+x92lu2bFH/zpDBjeJJZHoDMCGEEIUie1Ca037qetevX+fAgQN07ty5wNuSvWP15MmT2NjYWFSuSpUqeb7XkCFD8PX1Zd26dezatYvg4GCio6O5d+8eK1euZOXKlYwbN44lS5aY7Pj9+OOPLV42NLflXLPLfg9/f39q1KhhUbncOp6L06hRo1ixYgWJiYls2bKFESNGAFmjlfUz1YpqJnn//v0pX748UVFRLF26VF3ma/PmzeofYxJgCyGEMKVx48bq59lXkSkJGjZsqG6VUpBt08clzz33HF999VWB1fs4Hme2sf71lC1bln379llcztz+nPqVnx5X5cqVCQkJYc+ePfz+++8EBQWpy4MGBwcTHBzMnDlz2L59Oz4+Pmo5/etp3LgxK1euzNP9LJE9Ln3ppZd44403LCpna2trcVuKWufOnalcuTI3b95k9erVBgNh9AMv9bPHCputrS1jxoxh/vz5rF+/noULF+Lk5ERKSgpr1qwBwM/PT10yVgghhChO2WPMunXrFso9dDqdmpzNyMiwqK8rODiY69evW9x/lhcFHTua8/fffzNp0iT1Nb/zzjt069aNGjVq4OLiovaPLlmyhIkTJwLkuDJncXN2dmbgwIGsWbOG33//nUWLFqmrQenjLSsrK55//vkiac9LL72Ev78/V69eNejT1g92rFKlijrJTIgniSTbhRCiiFy6dIkjR47kudyKFSsMku3ZZ/XkNpsqp/PZZ2V5eXnlK4meF25ubrzyyiu88sorAFy4cIEtW7awcOFCbt26xfLly2natKnasZi9fTY2NgZL2ReU7Pdwd3fP1z30M2Qga8mmopgxnpPu3burCe5Vq1apyXb9H1BardbsaOWCZmNjw9ixY5k9e7baqens7KwG2NWqVVP3zxRCCCGya9iwIWXKlCEmJobg4GCSkpJynaldVNzd3WncuDGnTp3iwoULREREUK1atceut0yZMty6dYu0tLRCiXsslT3WjIqKyvHanM7r46yHDx9Sr169AkuWF5Tu3bvTvXt3AGJiYti9ezeLFy9m7969XLlyheeff96go1v/ehISEgrl+WSftaYoSr7u4enpqQ4EuX37dkE2L1+0Wi0jRoxgzpw57Nmzh6ioKMqXL8/FixfVJWuLcjuhl156ifnz55OQkMD69esZP348mzdv5sGDB4AMAhVCCFFy/PHHH+rnHTp0KJR77Nmzh5s3b+apjKIo/Prrr3z00UfqsYLupyzs2HHZsmVkZGRgZWVFUFCQ2X68nLYC0vcFloR4C7LiqTVr1hAfH8+2bdsYOnQoOp2OtWvXAtCjR48imzjUt29fKlasyO3bt1m6dCmdO3fm5s2bBAYGAjBu3DiT26oKUdrJd7UQQhSR7LPaFy1axJo1a3L80I/y27Bhg8FyRbVr11ZHKOo7qczJvif5o5o2bap+fujQoXy9psdRr149/vOf/3DkyBF1Nvpvv/2mnq9VqxZubm6F2r6CeA+yr1CQfal0SxX0/pRWVlZqMj0wMJCYmBgURVFn7HTt2pVKlSrlq+78tFW/lHxiYiLr16/nxo0b6h+NEmALIYQwR6PRqFvuxMfHG+35V9zGjh0LZM0I+u677wqkTn1cEhoaqm47VByyzzI+fvx4jtfmdF7/elJTU3OMSUuCMmXK8Pzzz7Nnzx4GDBgAwOnTp7l06ZJ6jf71XL16Nd97g+bE1taWBg0aAPmPS7MPUA0ODs7XLKyCjk31yfTMzEzWrVsH/DMIVKPRqANDi0L9+vXVLQT0gz/1S8hXr15dHXwhhBBCFKezZ8+yZ88eAKpWrZrjPuePQ99PaWdnx6pVq3Ltp9THQr/++qtBPfr4BQqmn7KwY8dz584BWasV5TRhJqc26PsCIyIiCA8Pz3MbCjre6tWrlzoAQD+bPSgoSB1M8TiDG/PaVisrK8aPHw9k9WknJCSwfPlydDodGo2GCRMm5LstQpRk0sMthBBFQFEUdbnJhg0bMmnSJF544YUcPyZPngxkdTBv3rxZrcva2lpdjnvnzp1mR4XqR5ua4+vrq84Q++abb4ptSaSqVauqS3Rm35PKyspK3ecnMDCQCxcuFPi9mzVrps7oX7x4scn9OXPTuHFjqlatCsDPP/9MQkJCnsrrB05A1h8UBUEfRKenp/Pbb78REhLC9evXDc7lR37a6uPjQ8eOHYGsTk0JsIUQQlhq2rRp6u+e9957j2vXrhVzi/7x8ssvq3tOzp8/n6NHjz52nfokb1xcnNGe0kWpUqVK6r6c69evN7svfGJiosFAyUf1799f7Zz7+uuvC7ydhSV7wjV7bKp/PoqisGDBgkK5t/4ef//9N7t27cpXHf379wfg2rVrbNmyJc/l9T9zBRWXNmnShPr16wP/JNn1g0A7duz4WKtC5Ket+oGgwcHB7Nu3T01mjB8/vsA7voUQQoi8Sk5OZuzYsWof3dtvv421dcEvTpyQkMCmTZuArFnPI0eOzLWfUj/Y9NKlSxw+fFitq0qVKmq/3vr1683+Xk5JSWH9+vVm21RUsWNGRgaQFcuac/v2bbZu3Wr2vD7egqy/BfKqoPsCra2tGT58OADbt2/nwYMHatLd0dGRQYMG5bvu/MRbEydORKPRkJiYyLp161i2bBkAnTp1onbt2vluixAlmSTbhRDCAjNmzECj0aDRaNQAIS8OHDigJjuHDh1qUZnevXvj7OwMGM6KB/jXv/4FZAXhkyZNQqfTGZWfN28eJ0+eNFu/u7s7U6ZMASAkJISpU6earEcvKiqKn3/+2aK2Z5d9aUZTIiMj+fvvvwHjfZbee+89rKys0Ol0DB06lBs3bpitJzMzk1WrVuV4zaO0Wi3vv/8+kDVLaezYsTkGj/Hx8Xz77bdGdbzzzjsA3Lhxg7Fjx5qdjabT6bh165bBsTJlyqh7bV65csXituekZcuW1KlTB8jq1NQH2Pb29gwZMiTf9VasWFH9PC9tzd6puXDhQgC6dOmS5321hBBCPF2qVavGN998A2QloDt06MDBgwdzLKMoSo5xB/yTVNNoNOzfvz9fbXNycuLXX39Fq9WSnp5Oz549DQZHmhMbG2v23Lhx49QBfG+//XauK+YcPHiQoKCgPLXbUvpBn3fu3OGtt94yec3UqVO5e/eu2Trq1q3LsGHDAFi7di3z5s3L8Z7Xrl1Tk7CF5fTp05w+fdrseUVR2L17N5A1iyf7fqQ9e/akVatWAMyePTvHgQYAf/31F/7+/nlq3xtvvKHG/xMmTFBnXpkTEBDAmTNnDI5NmTJFXTXqX//6F2fPnjVb3lTcrI/3rl69WmCDcfWDPY8dO8aaNWvUFQMedwl5fVvzEpc+//zzuLi4oCgKI0eOlEGgQgghSozz58/ToUMHdRubzp07qzHZox43nt24caOabLa0n3LIkCFqMtxcP+WNGzf4z3/+Y7L8O++8Y9Qnll1RxY76/rJLly4REhJidD4pKYmRI0eaHXAKWROYmjdvDsDChQvV5dpNiYmJMaorv/1rOdHHVampqaxevZqNGzcCMHDgQDW+zI/8xIa1a9emS5cuAHz44Ydq7Cdb9ognmiKEEE+hffv2KYACKJ988kmu13/yySfq9UuXLs3z/V588UW1/F9//WVxueHDhyuAYmVlpdy+fdvgXM+ePdU627dvr6xbt045ceKEsnPnTmX06NEKoLRq1Uq9ZtmyZUb1p6SkKK1bt1avady4sfLtt98qBw8eVE6dOqXs3btXWbhwoTJw4EDF1tZWad68eZ5fe+fOnRVHR0dl2LBhyqJFi5T9+/erdX/11VdK1apV1ftv2rTJqPz8+fPV825ubso777yj7NixQzl58qQSEhKirF69WnnttdeUihUrmnx/r127luOz0+l0ip+fn3pN7dq1la+++kptZ1BQkPLjjz8qI0aMUJycnJQyZcoY1ZGZman06NFDrcPHx0f5+uuvlYMHDyonT55Utm/frnz88cdKnTp1TH6/tW/fXgGUMmXKKKtXr1bOnz+vXLp0Sbl06ZISExOjXpf9+3bfvn05vu8zZsxQAEWj0Shubm4KoAwdOjTHMrm9V/Hx8Yq9vb0CKM2aNVMCAwOVsLAwta1JSUkm601KSlLboP/49ddfc2yLEEIIoZc9DgOUbt26KfPnz1d2796tnDx5Ujl+/Lji7++vzJw5U2nQoIF6nZWVlXLz5k2j+saNG2fx79Pc/PTTT4qNjY1aX8uWLZX/+7//U3bt2qWEhoYqoaGhys6dO5Uvv/zSIOYClCNHjhjVd/jwYcXOzk5t/6hRo5T169croaGhyrFjx5QtW7YoH3/8sdKoUSMFUBYuXGhUh6UxbufOnRVA6dy5s9G59PR0pWnTpmpdvXv3VjZv3qycOHFC2bx5sxqHtmjRIsf3MiYmRqlVq5Z6TadOnZSff/5ZOXz4sHLy5Enljz/+UObMmaP4+voqWq1WGTJkSJ7amVdLly5Vn9Onn36qbNu2TQkNDVUOHz6srF692iCeGzhwoFH5y5cvK56enuo1/fv3V1auXKkcPXpUCQ0NVbZv367MmjVLadOmjQIob731Vp5fz8aNGxWNRqMAir29vTJp0iRly5YtyokTJ5QjR44oGzZsUKZPn66+r/7+/kZ1rFixQm2jg4OD8vrrrys7duxQTp06pQQHByuLFi1SnnvuOaVWrVpGZX/66Se17JtvvqmEhoaqsd7169cNrq1evboCKOPGjcvxfb927Zr6mtzd3RVAsbW1Ve7fv59judzeq1GjRimAYmdnp/zwww/KX3/9pbY1KirKbL0vv/yywc9i9+7dc2yHEEIIkV/Z+3EmT56s/PXXX+rHkSNHlB07dihffvml0rNnT/V3JaC0adNGiY6ONlvv48az3bp1UwDFxsYm19/H2en7GT08PJTU1FT1eGpqqtKwYUO1Tf369VPjly1btih9+/Y16qfcv3+/Uf0FETvm5tixY2r97u7uyqxZs5SgoCDl6NGjyvfff6/UqVNH7WvVX3ft2jWjes6fP684Ozur1wwePFj57bfflNDQUOXo0aPKqlWrlHHjxilOTk5G5S9duqSW69mzpxIUFKRcvHhRjWPS09PVa/XPunr16rm+tpo1axrEW4Cybdu2HMtk/1vLlLzEhtmtWrXKIN5ydXVVEhMTc30NQpRWkmwXQjyVijLZnpSUpLi6uiqAUrdu3TyV/e2339T7zpkzx+BcbGysQZD66EfTpk2V0NBQ9eu1a9eavEd8fLwyePBgs/Vk/+jatWue2q8o/3SS5fSh1WqVzz77zGwdixcvVhwdHXOtx9bWVrl06ZJB2dwSyIqiKGlpacrkyZMN/rAx91GzZk2TdSQmJipDhw7Ntbyp77dt27aZvXf26/OSbM8euOs/TA1myOt7NX36dLOvLac2TZ48Wb3Ozc3NbGJeCCGEMOX333836HjL6UOj0Si9e/c2O8CxIJPtiqIoQUFBSuPGjS1qG6C0bdtWCQ4ONlvf4cOHDQYj5vSxfPlyo/KWxri5JTJv3ryp1K1b1+y9e/bsqezatSvX9/L27dtKx44dLXo9EyZMyHM780KfbM/to127dsq9e/dM1hEWFmbQmZzTx8yZM/P1erZu3WqQ1M8pht67d6/JOpYtW6Y4ODjkWN5Up+3Dhw/N/qw9er2lyXZFUQw6rMH0YIZH5fZenTp1Sh2c8uhHTm06evSowbWrVq3KtS1CCCFEfmTvx7Hkw8vLS5k1a5ZBstWUx4lnIyIiFK1WqwBKr1698lT2q6++Uu+7YcMGg3Ph4eFK7dq1c4wdd+zYoX5tauCpojx+7GiJmTNn5ljvW2+9ZRA3mkq2K4qihIaGWhS3myqvn2CV2/V5SbZ/8MEHBvWULVs21++l3JLteYkNs0tOTlY8PDzUa19++eVc2y9EaSbLyAshRCHbvHkz8fHxAHlewrtPnz44ODgAxks0ubu7c/DgQebPn0/z5s1xdnbGxcWFJk2a8MUXXxASEoKVlZV6vZubm8l7uLi4sHHjRoKDg3nppZeoW7cuLi4uWFtb4+npScuWLXn11VfZvn07f/zxR57aD1l7Mi5evJiRI0fSpEkTKlSogLW1Nc7OzjRo0IDJkydz6tQpPvzwQ7N1vPzyy1y9epWZM2fSvn17ypYti7W1NU5OTvj4+DBkyBB++OEHbt68ibe3d57baGNjw/fff8+ff/7Ja6+9RqNGjXBzc8PKygo3NzeaNGnCxIkT2bBhg9m94x0dHVm/fj179+5lzJgx1KxZEwcHB2xtbalatSr9+/fnxx9/NLkUa9++fdmzZw8DBw6kUqVK2NjY5Pk1PMrb21td6hTAw8ODPn36PHa9//3vf/npp5/o2LEjnp6eBt9jORkzZoz6+QsvvKB+XwshhBCW8PPzIywsjN9++42JEydSv359NR5wdXWlZs2aDBgwgC+++IIrV66wY8cOGjZsWCRt69SpE6dOnWL79u28+uqrNG7cmHLlymFjY4OzszPVqlWjd+/efPzxx/z111+EhITQoUMHs/W1adOGS5cu8cMPP9C3b18qVaqEra0t9vb2VK1alZ49ezJr1iz+/vtvde/MwlCpUiVOnTrF559/TsOGDXFwcMDd3Z02bdrw/fffs2PHDnUrnJxUqFCBAwcOsG3bNkaNGkWtWrVwdHTExsYGLy8v2rVrx1tvvUVQUBBLliwptNcDMGLECLZv387UqVPp0KEDNWvWxNHREVtbW6pUqcKAAQNYtWoVwcHBlClTxmQdPj4+nD59mtWrVzNkyBCqVaumxnwVK1akS5cufPjhh5w4cYKPP/44X+3s378/165dY86cOXTr1o3y5ctjY2ODg4MDNWvWpF+/fsybN4/r16/TtWtXk3WMGzeOK1eu8MEHH9C8eXPc3d2xsrLCw8ODNm3a8P7777Nz506jcs7OzoSEhPDGG29Qr149HB0d8/UaHvXokvGPu4Q8ZO0Hf/jwYUaMGEG1atWws7OzqFyrVq3UvWXd3d0ZPHjwY7dFCCGEyAutVoubmxvVqlWjY8eOvPnmm2zcuJEbN27w/vvvF8o+7XorV65Ut5HMaz9l9usf7aesVq0af/75JzNnzjQbO6akpKjXm+unLIrY8eOPPyYgIICePXvi4eGhxoKDBw8mMDCQOXPmWFRP8+bNCQsL45tvvqFbt26UK1dO7fNs1KgRr7zyCnv27DHYmkhv5cqVfPXVV7Rq1Qo3Nze02sdP1T0aXw0fPvyxv5fyGxva29ur2wKALCEvnnwaRSmgTbiEEEKUOCtXrlSTnJcvX6Z27drF3CLxtPrpp5945ZVXADh69KjBQAAhhBBCCCGKSnx8PBUqVCA5OZnJkyfz/fffF3eThBBCiKfC559/zkcffYS1tTUPHz7E3t6+uJskClH79u0JCQmhfv36nDt3rribI0ShkpntQgjxBFuzZg0AXl5e1KpVq5hbI55m+tHGDRs2lES7EEIIIYQoNmvWrCE5ORmAiRMnFnNrhPjHmTNnmDJlCm3atKFSpUrY2dnh5uZG27ZtWbhwIenp6UZlZsyYgUajyfXjwIEDBuW6dOmS4/WmVr7QW7ZsGa1atcLZ2RlPT0/69OlDSEhIjq/t0KFD9OnTB09PT5ydnWnVqpXRrFghxJNNURTWrVsHZK1OI4n2J1tYWJj6u0FmtYunQeGtRyKEEKJQ3bx5E09PT7PLcf/8889s374dgLFjx6LRaIqyeUKoDhw4wJEjRwCYNGlSMbdGCCGEEEI8rTIyMpg3bx4ALVq0oHnz5sXcIiH+ceDAAb777juqV69O/fr18fLyIjo6mkOHDnHkyBE2btxIYGCgwRYeTZo0Ydy4cSbru3nzJrt378bR0ZFmzZqZvGbIkCE4OzsbHa9cubLJ6998800WLFiAg4MDPXv2JCUlhT/++IPAwEA2bNjAoEGDjMps3LiR559/Hp1OR6dOnShbtix79uxh3LhxnDlzxuLlmoUQJdv169epUqWK2WXLP/74Y86ePQtg9v8t8eT48ssvgazl5MePH1+8jRGiCMgy8kIIUUotW7aM6dOn88ILL9ClSxeqV6+OTqfjypUrrFu3js2bNwNQvnx5zp07Z3bfSSEKQ3h4OKmpqZw7d46pU6cSHh5OhQoVuHr1quzXLoQQQgghisz9+/e5f/8+MTExzJ07l/Xr1wPw+++/4+fnV8ytE+IfV69eBTBalS4qKgpfX1/Onj3LwoULmTJlikX1vfvuu3z11VeMGjWKlStXGpzr0qULQUFBXLt2zeRewqbs3r2bHj16UKZMGQ4fPkydOnUAOHz4MF26dMHR0ZFr167h7u6ulrl//z41a9YkPj6ejRs3MnjwYPU1dejQgcuXL7Nv3z66dOliURuEECXXjBkzWLp0KSNHjqR9+/ZUqlSJ9PR0Lly4wPLly9m/fz8A9evX5+TJk9jZ2RVvg0WBSk5O5ubNmyQlJbF582ZmzJiBoii8/vrrLFiwoLibJ0Shk5ntQghRikVHR7Nw4UIWLlxo8nzFihUJCAiQRLsocp07dyY8PNzg2MKFCyXRLoQQQgghitQ333zDzJkzDY7169dPEu2ixDG39Vv58uV59913GTNmDHv37rUo2a4oirqt3JgxYwqkffpVIT788EM10Q7Qtm1bJk2axDfffMMvv/zCW2+9pZ77+eefiY+PZ+DAgWqiXf+avvrqKwYPHszcuXMl2S7EEyIiIoL//ve/Zs8/88wzBAQESKL9CXT06FG6du1qcKxq1arMmDGjeBokRBGTPduFEKKU6tevH4sWLWLQoEH4+Pjg4eGBtbU1ZcuWpWPHjnz11VeEhYXRtGnT4m6qeIq5uLjQtm1btm3bxtChQ4u7OUIIIYQQ4illbW1N7dq1+fDDD9U9Y4UoLWxsbAAMlpDPyf79+4mMjKRChQr4+vo+9v2Tk5PZu3cvgMm/6/TH/P39DY4HBASYLdO3b1/s7e3ZvXs3KSkpj91GIUTxmjhxIvPmzaN37954e3vj5uaGjY0N5cuXp2fPnixatIg///zT4tU0ROmk0WioVKkSo0eP5uDBg3h4eBR3k4QoErKMvBBCCCGEEEIIIYQQQpRAsbGx9OrVi+PHj7NixQqLZqpPnDiRJUuWMHXqVHVGenb6ZeQ//PBDYmJi0Gq1+Pj4MGjQIKpVq2Z0/enTp2natCleXl7cvXvX6HxiYiLOzs54eHhw//599bi7uztxcXGcO3eO+vXrG5Vr2bIloaGh/Pnnnzz77LO5vi4hhBBCiJJIlpEXJYJOp+PWrVu4uLig0WiKuzlCCCGEeIIpisLDhw+pVKkSWq0s9FSSSYwohBBCiKJQkuLDS5cuMWvWLHQ6HVFRUYSEhJCQkMCkSZMYNWpUruVTUlLYuHEjkPsS8p9//rnB12+//TYfffQRH330kcHxiIgIAKpUqWKyHicnJ9zd3YmNjeXhw4e4uLgQHx9PXFxcjuWqVKlCaGgo4eHhZpPtqamppKamql/rdDru379PmTJlJD4UQgghRKGyNEaUZLsoEW7dukXVqlWLuxlCCCGEeIpERkaa7fgTJYPEiEIIIYQoSiUhPoyKimL58uUGx15//XU+++wziwYCbN26lbi4OBo0aGB2W7lOnTrx0ksv0a5dOypWrEhkZCQbNmzg888/5+OPP8bV1ZU33nhDvT4hIQEAR0dHs/d1cnLiwYMHarJdXyanck5OTgA8fPjQbL1ffPEFM2fONP+ChRBCCCEKWW4xoiTbRYng4uICZH3Durq6FnNrhBBCCPEki4+Pp2rVqmr8UdTOnDnD4sWLCQ0NJSIigpiYGOzt7alfvz4jR45k0qRJ6r6cejNmzLCokzEoKIhOnToZHY+OjubLL7/E39+fiIgIHBwcqFGjBt27d2f27Nkm6/L392fOnDmcOnUKgGbNmvHOO+/Qt29fs/c/d+4cM2bMYP/+/SQkJODt7c3EiRN5/fXX8zVLTGJEIYQQQhSFgogP/fz8uHDhQp7KrFixglatWhkc69ChA4qikJmZSUREBJs2bWLmzJns2LGDwMDAXPc7XrlyJZDzrPZPP/3U4GsfHx/ef/99WrRoQa9evZgxYwavvPIKDg4OeXo9heG9995j2rRp6tdxcXFUq1ZN4kMhhBBCFDpLY0RJtosSQb/sk6urqwTKQgghhCgSxbXs5IEDB/juu++oXr069evXx8vLi+joaA4dOsSRI0fYuHEjgYGB2NraqmWaNGnCuHHjTNZ38+ZNdu/ejaOjI82aNTM6f+LECXr16kVMTAwNGjRg4MCBxMfHc/78eebPn28y2f71118zdepUrK2t8fX1xc7OjsDAQPr168fChQuZMmWKUZnDhw/TvXt3kpOTadWqFTVq1ODAgQNMnTqVkJAQ1q1bl+f3XGJEIYQQQhSlx4kPr127RlhYWJ7KJCUlmT1nZWVFzZo1mTZtGjVq1GDIkCG89tpr+Pv7my0TExPDzp070Wq1Fi05/6iePXvSokULQkNDOXr0KF26dAHA2dk51/YmJiYC/wyW1JfRlzMVyz1axhQ7Ozvs7OyMjkt8KIQQQoiikluMKMl2IYQQQgghilCfPn3o06cPtWrVMjgeFRWFr68vQUFBLF682CChPWjQIAYNGmSyvnfffZfdu3fj5+dn0KkJWTPae/fuTXJyMlu2bGHAgAEG548dO2ZUX1hYGG+//TZ2dnbs27ePtm3bAnDx4kXatWvH1KlT6d27N97e3mqZ9PR0Ro0aRXJyMvPmzWPq1KlA1pKjPXv2ZP369fTp04fx48db/D4JIYQQQpQmp0+fLrS69XHezp07SUtLMxiUmd26detIT0+na9eu+V4Ov06dOoSGhnL79m31WLVq1QC4ceOGyTKJiYk8ePAADw8PNXHu6uqKm5sbcXFx3Lhxg/r16xuV09dXvXr1fLVVCCGEEKIkyPtajkIIIYQQQoh8q1WrllGiHaB8+fK8++67AOzdu9eiuhRFYc2aNYDppUI/+eQT7t27x+zZs40S7YDRsqUACxYsIDMzk0mTJqmJdshaXvSDDz4gIyODBQsWGJTZtGkT165do3HjxmqiHbJmNH377bcAzJ0716LXJIQQQgghDGk0Gjw9PcnIyCA2NtbsdZYsIZ8bff36/dQB6tati52dHdHR0dy8edOozMmTJwF49tlnDY43btzY4Hx26enpnD17Fnt7e3x8fPLdXiGEEEKI4ibJdiGEEEIIIUoI/V7t5mYrPWr//v1ERkZSoUIFfH19Dc4lJyezcuVKnJycmDBhgsVtCAgIAGDo0KFG5/THHl2+NKcyzZo1o1atWpw9e5br169b3A4hhBBCCJHl6tWr6h7lZcuWNXvN4cOHcXBwYMiQIfm6T3R0NMHBwQAG2xM5ODjQrVs3ANavX29UbsOGDQD079/f4Hjfvn0Nzme3bds2UlJS8PX1xd7ePl/tFUIIIYQoCSTZLp5YaWlprF+/nokTJ9K4cWMcHBzQarU4ODjQuHFjJk6cyPr160lLSyvupgohhBBCEBsbq87+1ndM5kY/e2nEiBFYWVkZnAsNDeXhw4c0bdoUBwcHduzYwbRp0/j3v//N119/za1bt4zqe/DgAREREQA0bdrU6HzVqlUpW7Ys4eHhxMfHq8f//PNPAJN7xmc/fubMGYtel3jySawuhBBCGFq4cCF37twxOh4WFsbIkSNRFIWxY8caxXx6+rhw4MCBOe5lHhISwubNm8nMzDQ4fv36dfz8/EhMTGTAgAFGy9BPmzYNgM8//5xLly6pxw8fPsyPP/6Iu7s7EydONCjz0ksv4erqypYtW/j999/V43fv3mX69OkAvPXWW2bbKoQQQgihV5L7EWTPdvHESU9PZ/78+cybN4+oqCij8ykpKZw5c4YzZ86wZMkSypcvz7Rp05g6dao6m0wIIYQQorBdunSJWbNmodPpiIqKIiQkhISEBCZNmsSoUaNyLZ+SksLGjRsB00uFnj9/HoBy5coxaNAgtmzZYnD+/fff55dffmHEiBHqMX2i3cPDw2Dp0OyqVKnCvXv3CA8Pp1GjRgblzO0Nqj8eHh6e6+sSTzaJ1YUQQgjT5s6dy5tvvknjxo3x9vZGURTCw8M5ceIEOp2OTp068cUXX5gtv2rVKiD3JeQvXrzIhAkTqFChAs2aNcPd3V29T0pKCg0aNOCnn34yKufr68sbb7zBggULaNKkCT169CAtLY0//vgDRVFYunQp7u7uBmU8PT1ZsmQJw4cPZ+jQoXTp0oUyZcqwe/duHjx4wLRp0+jSpUue3yshhBBCPD1KQz+CzGwXT5Tz58/Tpk0b3n33XZM/dKZERUXx7rvv0qZNG7VTWgghhBCisEVFRbF8+XJ+/fVXAgMDSUhI4PXXX+fLL79Eq809TN+6dStxcXE0aNDA5Cx0/X6bW7duZefOnXz33XfcvXuX69ev8/bbb5OcnMy4ceM4ffq0WiYhIQEAR0dHs/fVJ+EfPnxocTlTZUxJTU0lPj7e4EM8OSRWF0IIIcybNWsWL7zwAomJiezatYstW7YQERFBjx49WLZsGfv27cPZ2dlk2WPHjnHx4kXKlStHz549c7xP69atmTx5MpUqVeL48eP89ttvnD17liZNmjB37lyOHz9OuXLlTJb9+uuvWbp0KfXq1eOPP/7g8OHD+Pr6cuDAAQYNGmSyzJAhQzhw4AC9evXi1KlTbN++HW9vb5YtW6au6iSEEEIIYUpp6UeQme3iiRESEsJzzz2X707ZkydP0rZtW3bs2EG7du0KuHVCCCGEeFL4+flx4cKFPJVZsWIFrVq1MjjWoUMHFEUhMzOTiIgINm3axMyZM9mxYweBgYHUqFEjxzr1S4Wam72k0+kAyMjIYNasWfz73/9Wz82ePZvw8HDWr1/P7Nmz1ZlQxe2LL75g5syZxd0MUQgkVhdCCCFyNmrUKItWNzKlVatWKIpi0bX16tXj+++/z9d9AMaPH8/48ePzVKZ9+/bs2LEj3/cUQgghxNOnNPUjSLJdPBHOnz9v9ofOrWJrvGr2wsWrIdZ2bmSkxvEw+izR13YRd/uowbXx8fE899xzHD58mPr16xdV84UQQghRily7do2wsLA8lUlKSjJ7zsrKipo1azJt2jRq1KjBkCFDeO211/D39zdbJiYmhp07d6LVas12ymaf+TRhwgSj8xMmTGD9+vUEBQUZlcmpvYmJiQC4uLgYlIuNjTVbzlQZU9577z11P1DIis2qVq2aYxlR8kmsLoQQQgghhBBCCEuVtn4ESbaLUi89PZ3Ro0cb/dA5etThma6zcStvvKyqe6XWVG08kbioU/y97x2SYi+p5+Lj4xkzZgxHjhyRfSGFEEIIYST7susFzc/PD2dnZ3bu3ElaWhq2trYmr1u3bh3p6el07drV7D7p1atXB7KWdvfy8jI6r585f/fuXfVYtWrVgKwl6BMTE03u237jxg2D+vXlYmNjuXHjBs8++6xFZUyxs7PDzs4ux2tE6SKxuhBCCCGEEEIIISxVGvsRZM92UerNnz+fU6dOGRzzrNaVFkO3mfyhy86tfFNaDN2GZ7WuBsdPnjzJ/PnzC7ytQgghhBA50Wg0eHp6kpGRoe65bkpuS8gD6j7uycnJpKamGp2/f/8+YDgD3t3dXU24PxpfAURGRnLv3j2qV6+Oq6urerxx48ZAVgxliv64qUS8eLJJrC6EEEIIIYQQQghLlcZ+BEm2i1ItLS2NefPmGRxz9KhDw14/YGVtb1EdVtb2NOz1A44edQyOz58/n7S0tAJrqxBCCCFEbq5evUpkZCSurq6ULVvW7DWHDx/GwcGBIUOGmK2rWrVqNG7cGEVRDJaK19Mf0yfl9fr27QvAhg0bjMroj/Xv39/iMqdOneLq1as0bNgw133oxZNFYnUhhBBCCCGEEEJYqrT2I0iyXZRqW7ZsISoqyuDYM11nW/xDp2dlbc8zXb4yOHbnzh22bNny2G0UQgghhMhu4cKF3Llzx+h4WFgYI0eORFEUxo4di5WVlcny+lntAwcONJhdbsr06dMBePvtt7l9+7Z6/PTp08ydOxeASZMmGZR54403sLKy4ocffuDIkSPq8UuXLjFr1iysra154403DMr4+flRs2ZN/vzzT4ORwomJibz66qsAvPXWWzm2VTx5JFYXQgghhBBCCCGEpUprP4Ik20WptnPnToOv3Sq2znUZCXPcKjTDrWKrHOsXQgghhHhcc+fOpXLlyjRr1ozhw4czbNgwWrVqRf369Tl69CidOnXiiy++MFt+1apVQM5LyOuNHDmScePG8ddff1G/fn369u1Lt27daNOmDffv3+fll19m2LBhBmXq1q3L7NmzSU1NpWPHjvTp04dBgwbRuHFjYmJimDdvHt7e3gZlbGxsWLlyJQ4ODkybNo02bdrw/PPPU6dOHQ4fPszQoUMZN25cPt4tUZpJrC6EEEIIIYQQQghLldZ+BEm2i1ItNDTU4Guvmr0eqz6vmr0Nvj5x4sRj1SeEEEII8ahZs2bxwgsvkJiYyK5du9iyZQsRERH06NGDZcuWsW/fPoN91LM7duwYFy9epFy5cvTs2dOi+y1dupTFixdTu3Zt9u/fz7Fjx2jWrBnLli1j8eLFJstMnTqVrVu30rZtW4KDg9mzZw8tWrTA39+f1157zWSZdu3acfz4cYYMGcLly5fZunUrnp6ezJs3j3Xr1qHRaCx7g0Spp9MpJCbrOHL0uMHxgo7Vjx4LJS4hk9Q0HYqiPFbdQgghhBBCCCGEKF6lNeenUaRXQpQA8fHxuLm5ERcXl+tyqNk5ODiQkpKift104G+4V2qd73bE3jrC6S3Pq19bWdvzyqxruDhqcXbU4uygNfpc/dpRi6tT1nEhhBBClFz5jTtE0ZNnVbySUnTcuJtBZFQ6Dx5mkpSqkJyiIzEl69+kVIWkFB1JKVn/JqcqJKboSEnN+hMzaLEPusxUtb6CjtW11vZ0fjks63MN2NlqcLDTYG+nxd5Wg72tBof/fe5gp8HJQUv5MtZUKmtNZa+sfx3sJXYXQgghMUdpIs9KCCGEeHIVds7PwcGBpKQki8tbGndY57uFQpQAqampBl9b27k9Vn02j5TPzEjl7+tpearDxVFLpbLWVCqX1YFXyeuff8u4WqHVyqwuIYQQQghRMmTqFO7EZBAZlZVU1yfXI6MyiInLfKy6dZmGcXRBx+q6jH/+FtApkJyqkJyqADqL6/Rw0VLRIGa3UWN3DxetrMgghBBCCCGEEEIUkcLO+WVP5BckSbaLUs3Ozs7ghyMjNe6x6kt/pLzW2i7PdTxM0hEWkUZYhHGS3tZGQ8VsM2myJ+IrlLHG2ko684QQQgghRMGLS8gkMiqDG3fT1cR65N0MbkWnk55ROPfUWtkazGwvCbH6o2If6oh9mMb5a8axu4Odidj9f8n4ch5WWEnsLoQQQgghhBBCFJjCzvnZ29s/Vn3mSLJdlGo+Pj6cOXNG/fph9NnHWlIiIfqcwdeObrXyXZcpaekK4bfTCb+dbnROq4FynlZU/l8HXkUvWeJSCCGEEEJYLi1d4da9DG78L5GeNUM9K7ken2j5bO+C4uBei8SYC+rXJT1Wf1RyqsLVm+lcvWkcu1tpoUIZw8GzlcpaU72iDZW9rGVGvBBCCCGEEEIIkUeFnfPz8fHJd105kWS7KNVatGhh8IMXfW0XVRtPzHd90dd2GnzdqUMLJo/0JCFJR0KyjodJOuPPk7I+z3zM/kudAndiMrkTk8kJE+f1S1xWq2BD3Wq2+FSzpXYVW2xtpCNPCCGEEOJpk5aucOF6KqfCUggLTyPybgZ37mWgUwr2PhoNVPC0orynNY4OWhztNDjaa3G01+Bgr8XJXouDvQZHu6xj6jk7LdMT2/Drin+S7QUdqw/u15r5X1QmOVVHSlrWEvIpaVl7xqufpylZX6cpPHiYya3oDG7dy+Deg8dbIj9TBzejM7gZbbwsgJuzlga17GhYy44GtWypW91OYnYhhBBCCCGEECIXz9RvVqg5v+bNm+e7rpxIsl2Uar1792bJkiXq13G3jxIXdQq38k3zXFfcnZPE3T5mcGz8qH706+Cca1lFyerEi4n/pwMv+7+372WQmv54PZ/Zl7jceTgRyJpRU6NSVvK9TjVb6lazpVZlScALIYQQQjxpMjMVLkakcSoshVMXUzl7JfWx48vsXBy1VC1vTZVyNlQtb03V8ln/VvayyXds2b/fc/y6Yqn6dUHH6v36PoenmxVglef6UtN03I7J5FZ0ulHsficmg4zHyMXHJegIOZNMyJlkAGysoU5VWxrWtqNBrawPT9e8t1kIIYQQQgghhHgShd9O59cdcZy/3wpYph4v6H6E3r17P2ZLTdMoilLAcx+EyLv4+Hjc3NyIi4vD1dXV4nJpaWlUq1aNqKgo9ZijRx1aDN2GlbXley9kZqQQuqEfSbGX1GMVKlQgPDwcW1tbi+sxR6fLORFfkMt6WmmhZmUbfP6XfPepZkvNSpKAF0IIIfTyG3eIovc0PyudTuHarXROhqVwKiyFM5dTSUp5vD/drK2gUtl/EunZE+tuztoCX/q8tMTqRvfTKUTHZj4St2cl5W9HZ5D4mM8BoLKXddbs99pZs9+rV7BBq5V4XQghisvTHHOUNvKshBBCiCdHxJ2sJPve0CQUBXSZaRz+tR1pydHqNcXZj2Bp3CEz20WpZmtry7Rp03j33XfVY0mxlzi7axINe/1g0Q9fZkYKZ3dNMvihA5g6dWqBdd5ptRq83K3xcremcR3j8w+TdFkdeCaS8dF5XOIyUweXI9O5HJnO9kNZM+CtraBmJRvqVrfDR03A22BjLR16QgghhBAlgaIoRN7NyJq5HpbC6Yup+R6Q6emq/V9C3TCpXrGMNVZWRRf/lZZY/VFWWg0VylhToYw1zeoanlMUhfhEncm4/cbddGIfWvbM9EvQBx7NitedHTTUV5eet+OZGrY42GkL+qUJIYQQQgghhBDF7tEku57WypYqjV/i6pEv1GOloR9BZraLEuFxRqWmp6fTpk0bTp48aXDc0aMOz3SdnePyEnF3TvL3/ulGP3TNmjXjyJEj2NjY5KkthSEtXeH2vQxuRqdzMzqDy5FpXIxIIyIqg8f56bWxhlqVshLvPtX/ScBbF2EHrBBCCFEcZDZM6fGkP6s7MRmcupjyvwR7KjFxeRtkqdGAdxUbmvjY413FNiuxXt4GZ4eSk6R90mP17BRF4U5MJueuZi3zf/ZqKtdupecrZtdqoU4VWxrU+t/y87Xt8HKXsfJCCFFYnvSY40kiz0oIIYQovcwl2bOzt8ngfMBgwq/8aXC8OPoRLI07JNkuSoTHDZTPnz9P27ZtiY+PNzrnVrEVXjV74+zVABs7N9JT40iIPkf0tZ1G+zUAuLm5ERISQv369fP1WopKUoqOyzfSCAtP41JEGmERady4WwAJ+Mq2NKptR9O69jzrbYdTCeqsFUIIIQqCdNCVHk/as7ofl5mVXL+YlVy/fS8jz3XUqGhD07p2NPWx59k6drg6lfy9v5/GWF0vMVnH+WupnLuayrmraZy/lkpyav4C9nKeVjSsZUeLeva0beSAm3PJf/ZCCFFaPGkxx5NMnpUQQghR+kTcSWfl/5LsOjN/Ejvaa/Dr4sLQbi7cjAgrEf0IkmwXpUpBBMohISE899xzJn/4LOXm5sb27dtp165dvusoTonJuqyZ75FZSfiL/0vA55dWC89Ut6VpXXua1bWnQS072ftdCCFEqScddKVHaX9WKWk6jp//38z1i6mE307Pcx2VvKxp6pM1ELJJHXs83UpnglVi9SyZmQpXb6UbzH6/ez9vKxpAVpze1MeeDk0c6NjYsdR+XwghRElR2mOOp4k8KyGEEKL0iIhKZ+X2nJPsDnYaBnfNSrJnH1ReEvoRJNkuSpWCCpTPnz/PmDFjjJaptESzZs349ddfS80sGUsl/C8BHxaelYS/GJ7Gzej8JeBtbTQ0rGVLs7r2NH3GHp+qtkW676cQQghREKSDrvQojc9KURT+upJK4JFE9p9MIiklb39ulXGzolnd/yXXfeypUObJWTpcYnXTomMz1OT7uatpXLqRhs6yrd+BrO0EGtSyo2MTBzo2cXyivmeEEKKolMaY42klz0oIIYQo+R4nyZ5dcfcjSLJdlCoFGSinp6czf/585s2bR1RUVK7XV6hQgalTpzJ16tQSt+9jYUlI0mUl3iOyku9hEWn5WsbUyV5DY5+sWe9N69pRo6INGo0k34UQQpRs0kFXepSmZ3UnJoPAo4kEHk3kVh4GNro6aWla116dvV6lnPUTHU9JrJ675FQdf19Py0rAX03l/NVUEpIt/7O9TlUbOjV1pGNTR6qVfzreMyGEeFylKeZ42smzEkIIIUqu+3GZLA+IIyAkwewgckuS7NkVZz+CJNtFqVIYgXJaWhpbtmxh586dnDhxgosXL5KSkoK9vT0+Pj40b96c3r17M3DgQGxtbQvknqXZwyQdlyLSOHs1lZN/p3D+WioZeVzR0sNFS5P/LTnfrK49FcvKrBohhBAlj3TQlR4l/Vklp+gIOpVE4JFETl9KtaiMk72GZ+vYq/uu16xkg1b75CbXzZFY3XI6nUL4nXTOXU3j2Llkjp9PITXdsj/jq1e0odP/ZrzXriIDY4UQwpySHnOIf8izEkIIIUqe5BQdv+15yLrd8aSkmv571cFOw+AuLgztblmS/VHF0Y8gyXZRqkigXPKkpOn463Jq1h6jYalcjEwjr/9bVChj9b9Z7/Y09Sm9e4wKIYR4skjcUXqUxGel0yn8eSmVXUcSOXA6yewfkXpaDTxbx44Wz8g2PKJgJKfqOH4+heDTSRz+K9nirQoqlrVWl5qvV8P2qRzkIYQQ5pTEmEOYJs9KCCGEKDkyMxV2HE5k2bYH3I83PZX9cZPsxUmS7aJUkUC55HuYpOP0xZT/Jd9TCL+T92Xna1S0ydqD9Jmsme8OdtpCaKkQQgiRM4k7So+S9Kxu3k1Xl4mPup/78j/VK1jTs40zvq0c8XKX1X5E4UhLVzj5d1bi/dCZZOITLdvsvYybFR2aONCpiSPPetvJABAhxFOvJMUcImfyrIQQQojipygKR86msHjzA8Jvp5u8xs4ma7n44b6lL8muJ8l2UapIoFz63HuQwemLqZwMS+FkWAp3Leh0zs7ORkOLevZ0bOJAm0YOuDqVzv9shRBClD4Sd5Qexf2sEpJ1BJ3MWib+ryu5LxPv4qilWwtHerZx4pnqtrJktyhSmZkKf15OJfh0EgdPJxMTZ1l87uaspf2zDnRo4kizuvbY2sj3rRDi6VPcMYewnDwrIYQQoniFhafy4+8PzG6np9FA7zZOjO/nhpdH6Z58IMl2UapIoFy6KYrCrXsZnApLVWe+P0iwbFYNgFYLTerY0aGJI+0bO8jsLyGEEIVK4o7SozieVaYua6Zw4NFEgk8nk5bL3thaLbSqb0+vNs60beQgiUpRIuh0Cheup3HgVBLBp5O4E2NZ4t3JXkObRg4819aZpnXtZMCIEOKpIfFh6SHPSgghhCged2Iy+HnLA/aGJpm9plV9e17xc6dW5YLZM724SbK9hDhz5gyLFy8mNDSUiIgIYmJisLe3p379+owcOZJJkyZhY2NjUGbGjBnMnDkz17qDgoLo1KmTwbHjx48ze/ZsDh48SHR0NE5OTjRq1IgXX3yR8ePHm+wsyczM5JtvvmHJkiVcvnwZZ2dnunbtysyZM6lXr57Z+/v7+zNnzhxOnToFQLNmzXjnnXfo27evJW+NAQmUnyw6ncK1W+mcupjCyb9TOHM51eK9JAGeqWFLh8ZZs2uqlbfJvYAQQgiRBxJ3lB5F+awi7qSz60gifxxL5N6D3BOTtSrZ0LONE74tnfB0kxV6RMmlKAqXb6QTfDqJ4FNJFm8HVa28NQM6udCrjRNODrL9kxDiySbxYekhz0oIIYQoWg+TdKzcEcfmoIekm/lz0ruKDf8a7EHzZ+yLtnGFTJLtJcS3337La6+9RvXq1fH29sbLy4vo6GgOHTpESkoKnTt3JjAwEFvbf0Z5bN68mc2bN5us7+bNm+zevRtHR0eioqJwdnZWz23cuJHnn3+ezMxMmjVrhre3N9HR0QQHB5ORkcHIkSNZtWqVQX06nY6hQ4eyadMm3N3d6d69O/fu3ePAgQM4ODiwb98+WrVqZdSOr7/+mqlTp2JtbY2vry92dnYEBgaSnJzMwoULmTJlSp7eJwmUn2yZmQphEWmc/Pt/yfcrqegsnPhevYI1HRo70qGJAz7VZDlWIYQQj0/ijtKjsJ/VwyQd+0IT2XUkkQvX03K93s1ZS/cWjvRq64x3FRuJS0SpFH77f4n300lcijS9t1529nYaerRyYlBnZ2pWejJmJwghxKMkPiw95FkJIYQQRSMtXWFz0ENW7YznYZLphE45DyteHOCOb0tHtNonr49Eku0lxNWrVwGoVauWwfGoqCh8fX05e/ZsnpLT7777Ll999RWjRo1i5cqV6vGMjAwqV67M3bt3WbVqFSNHjlTPXbhwgQ4dOnD//n327t1L165d1XM///wzL7/8MnXq1CE4OJjy5csDWYn7oUOH4u3tzYULF7C2/mdZ77CwMBo0aIC1tTX79u2jbdu2AFy8eJF27doRFxfHhQsX8Pb2tvh9kkD56RKXkMmRs8kcPJ3M8QspuS7PqlfOw4r2jR3o0NiRZ73tsLJ68v7zFkIIUfgk7ig9CutZXbmRxqqd8Rw6k2R2VLaelRbaNHKgV2snWjd0wMZa4g/x5Lh9L0NNvJ+7mvuAk2e97RjYyZmOTR2xllhcCPEEkfiw9JBnJYQQQhQunU5h34kkftn6wOyWZE4OGkb1cmNwV5cnejs9S+MO2Ri5kD2aZNcrX7487777LmPGjGHv3r0WJdsVRWHNmjUAjBkzxuDc33//zd27d6lbt65Boh2gXr16jB49mm+++Ybjx48bJNvnzZsHwFdffaUm2gGGDBnCgAED2Lp1K1u2bGHIkCHquQULFpCZmcmUKVPURDuAj48PH3zwAdOmTWPBggUsXLgw19cknk5uzlb0auNMrzbOJKfqOH4+hYN/JnH4r2QSk80n3u/GZrJpfwKb9ifg6qSl3bMOdGjsQIt6sj+qEEIIISyXkqaw/6T5PcYA6lS1oVcbZ7q1cMTdRZaJF0+mimWtGe7rynBfV+49yCDoZBL+wQlERJkehXLmcipnLqfi6RpLvw7O9O3gjJe7dCsIIYQQQgghxJPg9MUUfvz9AWERpgdjW1vBwM4ujO7tipuz9JXoycZrxUi/V3v2JeRzsn//fiIjI6lQoQK+vr4G5+zs7Cyqo0yZMurn165d48KFCzg4OJjcZ33o0KFA1t7s2QUEBBict6SMEOY42Gnp1NSR98eX5fcvq/DlFC/6d3TG0zXn/57iE3XsPJzIhz/cY9D0G8z4KZo9xxNJSLZwfXohhBBCPLXq17SlSjnjBKGHq5Zh3V34+YMK/PheRQZ3dZFEu3hqlHW3Zkg3V5Z+XJE5r5ejYxMHtGZC8vvxOlZsj2fEh7eY8VM0py+mIIvmCSHE4zlz5gxTpkyhTZs2VKpUCTs7O9zc3Gjbti0LFy4kPd14648ZM2ag0Why/Thw4IBBuS5duuR4/c6dOw2u1+l0BAcHM336dJo3b46Liwt2dnbUrl2bSZMmce3aNZOvaf/+/Tnep02bNgX3BgohhBAi367fTuf97+8y7eu7ZhPtXZs7svTjirw61EMS7Y+QIejFJDY2lrlz5wKYTHSbol82fsSIEVhZGX4j16pVi9q1axMWFsbq1auNlpFfuXIlHh4e+Pn5qcf//PNPABo2bKgm/rNr1qwZkBXs6z148ICIiAgAmjZtalSmatWqlC1blvDwcOLj42U5J5EnNtYaWtZ3oGV9B9543oML19M4eDqJg38mczPa/BqvKakKB04lc+BUMtZW0LSuPZ2aOtK5qSPOjjKmSAghhBCGNBoNvdo48cvWOGysoW0jB3q3caZlfXvZpkY89TQaDc2esafZM/ZEx2bgfzCBgEMJxMYbD2rV6VDj8OoVbRjYyZkerZxwcpAYXAgh8urAgQN89913VK9enfr16+Pl5UV0dDSHDh3iyJEjbNy4kcDAQINJO02aNGHcuHEm67t58ya7d+/G0dFR7eN71JAhQ3B2djY6XrlyZYOvr169SqdOnQCoUKEC3bp1w8rKimPHjvHjjz+yevVqtm/fTocOHUzep3bt2ibP1a5d2/SbIYQQQogiEROXybJtD9gRkojOzPjpRt52TPJzp15Nyyb9Po0k2V5ELl26xKxZs9DpdERFRRESEkJCQgKTJk1i1KhRuZZPSUlh48aNgPES8gBWVlYsX76cfv36MWrUKObOnUudOnW4e/cuwcHB1K9fn2XLluHp6amW0SfNq1SpYvKe+uPh4eFGZTw8PHBycjJb7t69e4SHh9OoUSOT16SmppKamqp+HR8fb/a1i6eTVquhQS07GtSy4xU/d67fTufg6WSC/0zicqTxaG69jEw4fj6F4+dTWPhbLO0bZ+2x2ryePVZa6TwXQgghRJaerZ1wdtDStYUjrk4yIlsIU7w8rHmxvztjnnPjwKkkth5I4K8rqSavDb+dzjfrYvlp8wN6tnZiQCdnalaybBU3IYQQ0KdPH/r06WO0JWVUVBS+vr4EBQWxePFig60oBw0axKBBg0zW9+6777J79278/PxMJtQB5syZQ40aNXJtm0ajoUePHvznP/+ha9euaDRZ/SupqalMmjSJZcuWMWrUKC5fvmxyQk+HDh1YtmxZrvcRQgghRNFITtGxbnc8v+15SEqq6Sx7tfLWvOznTrtGDurvfmGaJNuLSFRUFMuXLzc49vrrr/PZZ5+hNbc2XzZbt24lLi6OBg0amJxRDtC+fXuCgoLw8/Pj5MmTnDx5Eshapr5Hjx5GwXpCQgIAjo6OJuvTJ9MfPnxocRlz5R71xRdfMHPmTLPnhchOo9FQs5ItNSvZMqaPG3diMjj4ZxLBp5M5eyUVcytWpqUr7AtNYl9oEmXcrPBt6UjPNk7S6SeEEEIIvDysGdjZpbibIUSpYGOtoXtLJ7q3dOLKjTS2HEhg97FEUtKMA/HkVIUtBxLYciCBJnXsGNjZhfaNHbCWVSOEECJHj/bb6ZUvX553332XMWPGsHfvXoNkuzmKorBmzRrA9KSdvKpduzaBgYFGx+3s7Pj+++/ZtGkTERERhISE0Llz58e+nxBCCCEKR6ZOYefhRJb4PzC5ehmAh4uWcX3d6NveWVb/s5Ak23Ph5+fHhQsX8lRmxYoVtGrVyuBYhw4dUBSFzMxMIiIi2LRpEzNnzmTHjh0EBgbmOopUv4R8TgHymjVrmDBhAm3atGHNmjU0aNCAW7duMWfOHObOncu+ffsICQmxeH/3wvTee+8xbdo09ev4+HiqVq1ajC0SpUmFMtYM7ebK0G6uxD7MJORMMgdPJ3EyLIV0M6vNx8Rlsm73Q9btfkjdarb0bONEtxaOsreIEEIIIYQQeVC7ii3TRnryyiB3dh3JSqrfuGs6CD99KZXTl1Ip42ZFvw7O9OvgTBk3ib+FECKv9LPFsy8hn5P9+/cTGRlJhQoV8PX1Lcym4eDggI+PD8ePH+fWrVuFei8hhBBC5N+5q6ks/C2Wi2b2ZLe31TDc14Xhvq442svWYHkhyfZcXLt2jbCwsDyVSUpKMnvOysqKmjVrMm3aNGrUqMGQIUN47bXX8Pf3N1smJiaGnTt3otVqzS45f+nSJcaNG0e5cuXYtm2bujxUnTp1+PHHH7l16xbbtm1jyZIlTJ48GUC9xlx7ExMTAXBx+WfGT25lzJV7lJ2dXYlI+ovSz8PFir7tnenb3pnEZB3HziWz70QSR84mk5FpukxYRBphEWks2hhLm4YO9GrjROuGMttGCCGEEEIISzk7ahnSzRW/Li6cDEthy4EEDp9JNrnPX0xcJssD4li5I46OTRzx6+JMI2/7om+0EEKUQrGxscydOxeAvn37WlRGP2lnxIgRWFmZH+T0yy+/EBMTg1arxcfHh0GDBlGtWrU8tU+n06lbUFaoUMHkNZcuXeK9994jJiaGsmXL0qFDB3r37m3Rap9CCCGEeDwxcZks3hTLH8dM5/W0GniunRPj+rpR1l3Sxvkh71ouTp8+XWh16/dM2rlzJ2lpaWZHp65bt4709HS6du1qdn/1tWvXkp6eTu/evU3uwzR8+HC2bdvGgQMH1GS7Pni+ceOGyTr1x6tXr64e05eJjY0lMTHR5L7tpsoJURScHLR0beFE1xZOxCVksjc0iV1HEs2O1MrIhIN/JnPwz2TcnbV0a+lErzZOeFexkT1IhBBCCCGEsIBWq6FFPQda1HMg6n4G24ITCDiUwIME4yUJM3Ww/2QS+08m0ayuHRP6u9OglgzCFkKI7C5dusSsWbPQ6XRERUUREhJCQkICkyZNMjsJJ7uUlBQ2btwI5L6E/Oeff27w9dtvv81HH33ERx99ZHF716xZw927d/Hy8qJdu3YmrwkJCSEkJMTgWKNGjdi4cSN16tSx+F5CCCGEsFx6hsLGfQ/5dXscyWb2ZW/T0J6XB7nL1ruPSZLtxUij0eDp6UlERASxsbGUL1/e5HWWLCGvT3C7ubmZPK8/Hhsbqx5r3LgxAGfPniU9PV1dkkpPv+f7s88+qx5zd3enWrVqREREcOrUKTp06GBQJjIyknv37lG9enVcXV3NtleIwubmbIVfFxf8urhw7VYagUcS2X08iZg409PdHyTo+H3fQ37f95BalWzo2cYJ35ZOeMoyl0IIIYQQQlikvKc1Ewe6M6aPGwdOJbHlwEPOXTU98PVkWConw6Jo09CeCf3dqVNVOneEEAIgKiqK5cuXGxx7/fXX+eyzzyyaCb5161bi4uJo0KABTZs2NXlNp06deOmll2jXrh0VK1YkMjKSDRs28Pnnn/Pxxx/j6urKG2+8keu9IiMjefPNNwH49NNPjVaxdHNz45133mHIkCFqUv306dN88MEHHDlyhJ49e3L69Gmz/ZkAqamppKamql/Hx8fn2i4hhBDiaXf0XDLfrY81u+VX9QrWTBnuSfNnZMWxgiDJ9mJ09epVIiMjcXV1pWzZsmavOXz4MA4ODgwZMsRsXfplmkJDQ02eP378OIDB3vA1a9akXr16XLhwgYCAAAYNGmRQZsOGDQD079/f4Hjfvn1ZtGgRGzZsMEq2mysjRHGqWcmWfw225aWB7pz4O4VdRxI5+GeS2f3dr95K54ffH7B48wNa1benZxtn2jVywNZGZrsLIYQQQgiRG1sbDb6tnPBt5cSlyDS2HHjInmNJpKYbz6Y4cjaFI2fv0KmpA+P6usmMCiFEqeXn58eFCxfyVGbFihW0atXK4FiHDh1QFIXMzEwiIiLYtGkTM2fOZMeOHQQGBhr07ZliyaSdTz/91OBrHx8f3n//fVq0aEGvXr2YMWMGr7zyCg4ODmbrSExMZPDgwdy7d49BgwYxadIko2uaNm1qlPDv1q0bBw8epGvXrgQHB/P999/z3nvvmb3PF198wcyZM82eF0IIIcQ/bt5N57sNsRw5m2LyvJO9hnH93BjU2UW21S1AGkVRTK8dIArEwoULGTZsmNGeRWFhYYwbN46jR48yZcoUFi5caLL8p59+yieffMILL7zAmjVrzN7n5MmTNG/eHIDvv/9eXSoe4MiRI/j6+pKYmMgff/yBr6+veu7nn3/m5Zdfpk6dOhw8eJBy5coB8PvvvzNkyBC8vb25cOEC1tb/jMsICwujQYMGWFtbs3//ftq0aQNkLXPVtm1b4uLiuHDhAt7e3ha/T/Hx8bi5uREXFycz4kWRSEjSsf9kEruOJJidbZOds4OGri2ylpmvV8NWlpkXQohSTOKO0kOelRBPjodJOnYeTuC33Q/Nrjal0UD3Fo6M6+tG5XI2Jq8RQojCUBAxR5MmTfjzzz/zVGbfvn106dIl1+v0/XT9+vXD39/f7HUxMTFUrFiRzMxMwsPDzW5HmZOWLVsSGhqaY9vS09MZOHAgO3bsoEOHDgQGBuaYmDclICCAfv360blzZ/bv32/2OlMz26tWrSrxoRBCCJFNcoqOVbviWb8n3uQkQ40GnmvrxMSB7ni4yGq+lrI0RpRkeyGrUaMGkZGRNG7cGG9vbxRFITw8nBMnTqDT6ejUqRMBAQEm91kHqFu3LhcvXiQgIIA+ffrkeK933nmHOXPmANCgQQPq16/PrVu3OHz4MDqdjldeeYUff/zRoIxOp2Po0KFs2rQJDw8Punfvzr179wgKCsLe3p59+/bRunVro3vNnz+fadOmYW1tTY8ePbC1tSUwMJDk5GS++eYbXnvttTy9T9KRKorTjbvpBB5NJPBoInfvm+74y65qeWt6tXaiZxsnyrrLAiFCCFHaSNxResizEuLJk5qmw/9gAqt3xpvc1x1Aq4XebZ0Y85wb5T0l3hZCFL6SHnMoioKrqyspKSkkJiZia2t6FZDvv/+eV199la5du7J379583WvkyJGsWbOG1atXM2LECKPzOp2O0aNHs2bNGpo0acK+fftwd3fP830uXrxI3bp18fHxISwszOJyJf1ZCSGEEEVJURT2hibx46YH3HtgOrdRr4Ytrw334JkadibPC/Mk2V5CrFq1iu3btxMaGsqdO3dITk7G09OTJk2aMGLECMaMGWN2v6Vjx47RunVrypUrx82bNw1ml5uzadMmfvjhB06cOEFcXBwuLi40adKEl19+2WSADJCZmcmCBQtYsmQJV65cwcnJia5duzJz5kzq169v9l7+/v7Mnj2bU6dOAVlLQ02fPp1+/fpZ8M4YKimB8rFjx1i0aBH79+/n9u3beHl50aVLF95//33q1atXbO0SRUOnUzh9KZXAI4kcOJ1ESmrO/z1aaaFTM0f8OrvQoJbMdhdCiNKipMQdInfyrIR4ciWn6NgU9JB1fzzkYZLppLuNNfRt78yo3m6UcZPZF0KIwlMaYo7q1asTERHBnTt3KF++vMlr2rVrx+HDh1myZAkTJkzI132ee+45du7cyZYtWxgwYIDR+VdffZXvv/8eHx8fgoOD1VUy8+ro0aO0adOGpk2bcvLkSYvLlYZnJYQQQhSFy5FpLPwtlr+upJo87+Gq5ZVB7vRo5YRWK7mL/JBkuyhVijtQzszM5O2332bBggWY+pFwcXFhx44dtG/fvsjbllfh4eF88803BAQEEBkZiZ2dHbVr12b48OG8+uqrODo6FncTS4XkFB0HTiex60gipy+a/mWVXZ2qNgzu6kLX5k6yt7sQQpRwxR13CMvJsyoYMqBUlGQJSTo27I1n/Z6HJJsZ7Gpro2FQZ2dG9HTFzVmS7kKIglfSY46rV6/i7e2Ni4sL9+/fx8rK+P/Cq1evUrt2bRwcHLhz506+Xkd0dDQ1a9YkMTGRyMhIo2XoP/zwQ2bNmkW1atUIDg6mWrVq+X5N06dPZ/bs2UycOJGff/7Z4nIl/VkJIYQQhS0uIZMl/nEEHExAZ+JPKCstDO7qwpg+bjg7mJ7sKyxjadwh77J46ul0OsaNG8fXX3+NoiiMGTOGnTt3EhISwnvvvYdWq+Xhw4eMHz/eYI+oksjf359nn32WefPmERYWRlJSErGxsYSGhjJ9+nSaNm3K5cuXi7uZpYKDvZZebZyZ92Z5Vn9WiQn93KjkZX51iUuR6Xy54j7Pf3CTX7Y8IDrWxMYoQgghhBBFKDMzk6lTp9KmTRuWLVvG9evXSU1N5caNG6xcuZLWrVtz6NCh4m6mRcLDw3nrrbd45plncHJywtPTk5YtWzJ79mySkpIeq+7Q0FA+/fRTevbsSZUqVbCzs8PZ2RkfHx8mTJjAwd2xGCwAAQAASURBVIMHi6ydBdmW0sLZUcv4fu6s/qwSL/Rwwc7EwNW0dIXfdj9k5Ee3WOL/gAQzM+GFEKI0W7hwIXfu3DE6HhYWxsiRI1EUhbFjx5pMtAOsXLkSgIEDB+bYGRwSEsLmzZvJzDRcavb69ev4+fmRmJjIgAEDjBLt8+fPZ9asWVSoUIHdu3dblGj/+uuviYyMNDimKAo//vgj8+fPR6PRMHny5FzrEUIIIQRkZipsCXrI2Bm38Q82nWhvUc+eXz6syOQhHpJoL0Iys12UCMU5KvWLL77g/fffR6PRsHTpUsaNG2dwXj9qF+C3335j2LBhRdo+S506dYr27duTnJyMs7Mz7733Hl27diU5OZm1a9fy008/AeDj40NoaCguLi7F3OLSR1EUzl1NY+fhBPYcTyI13fx/n1otdGziiF8XZxrVtpMl5oUQogSR2TClhzyr/NPpdIwdO5ZVq1YBMGbMGEaNGoWrqyv+/v58+eWX6HQ6vL29OXv2LHZ2JXfvNn9/f0aPHk18fLzJ8z4+PgQEBODt7Z3nujt16kRwcHCu140dO5affvrJ7B65BdHOgmxLaRYTl8nqXXFsO5hAupnxq84OGp7v4crgLi442EsHkhDi8ZWEmKNGjRpERkbSuHFjvL29URSF8PBwTpw4gU6no1OnTgQEBODs7GyyfN26dbl48SIBAQH06dPH7H2WLVvGhAkTqFChAs2aNcPd3V29T0pKCg0aNGDv3r0Gy8OfPn2aZs2aoSgKbdu2xcfHx2TdL730Eh06dDB4TTdu3KBZs2bUrFmTlJQU/vrrL65du4ZWq2XBggVMmTIlT+9TSXhWQgghRFH782IKC9fHcvVmusnzFcta8++h7rRr5CC5iAIky8iLUqW4AuWzZ8/SrFkz0tPTmTp1KvPmzTO65v79+3h5eakz4JctW1Zk7csLfeectbU1Bw4coG3btgbnZ8+ezfTp0wH45JNPmDFjRjG08skRn5jJjpBEthx4yJ2YzByv9a5ig18XF7q1cMTOVjoDhRCiuEkHXekhzyr/ZECpZby9vbly5QqVKlVi2LBhdOzYkWrVqpGZmcnhw4eZO3cuN2/eBGDEiBGsXr260NpZUG15UkTdz2Dljjh2HE5EZ2Yiu7uzlhG9XBnQ0VnibCHEYykJMceqVavYvn07oaGh3Llzh+TkZDw9PWnSpAkjRoxgzJgxaLWm/687duwYrVu3ply5cty8eRNra/Mr8124cIGFCxdy9OhRIiMjiY2NxcnJiXr16jFs2DAmT56Mg4ODQZn9+/fTtWvXXF/D0qVLGT9+vPr1woULCQwM5Ny5c9y9e5f09HQqVqxIx44def3112nZsqVlb042JeFZCSGEEEXl7v0Mftz8gH2hpldKs7fVMLKXK8N9XWV720IgyXZRqhRXoDx48GA2bdqkjuI1d+/atWtz9epV2rZtS0hISJG1z1L6P6oA/vWvf/HDDz8YXaPT6WjYsCEXLlzA3d2du3fvYmNjU9RNfeJk6hSOnE1m076HnAzLeZsBVyctfds7M6CTM+U9zf/hK4QQonBJB13pIc8qf2RAqeX69evH2LFjGTJkiMllee/du0f79u25ePEiAEFBQXTq1KlQ2llQbXnS3Lybzortcew+noS53osyblaMfs6VPu2csbGWDiYhRN5JzFF6yLMSQgjxNMjaSiue1bviSUkz/YdQ1+aO/MvPnXKSayg0sme7ELkICwtj8+bNAEycODHHHxR3d3cAoqKiiqBlead/HQATJkwweY1Wq2Xs2LEAPHjwgH379hVF0554VloN7Z91ZM4b5VnyUUUGdHTG3tZ0B198oo41gfGM+ugWnyyO5vTFFGS8kxBCPH3OnDnDlClTaNOmDZUqVcLOzg43Nzfatm3LwoULSU83XhJsxowZaDSaXD8OHDhgVPb48eMMHz6cSpUqYWNjg7u7Ox07dmTp0qVmfw9lZmYyf/58GjVqhIODA15eXgwfPpwLFy7k+Nr8/f3p3Lkzrq6uuLq60qVLFwICAvL3Rol8+/jjj0lPT8fd3d1s8tnT05MaNWoAqMnbkubYsWPqsuoTJ040SmADvPXWW9SrVw+ABQsWmPz5ycm2bdsYPny42f1vy5Yty9y5c9WvN2zYUGjtLIi2PIkql7PhvfFl+eXDinRu5mjympi4TBasjWXsjFvsOJxAZqbE2EIIIYQQQojSR1EUDv2ZxIRPb7HEP85kor1WJRvmv1mOjyaWlUR7CSFPQTy1fv31V7WDWZ+ENiczM2uZ8JyW4SpOBw8eBMDJyYnmzZubva5z587q54cOHaJnz56F3ranSY2KNrw5wpOXBrqz43ACm4MSuH3PeKNJnQLBp5MJPp1MrUo2DOrigm8rR+xl6UshhHgqHDhwgO+++47q1atTv359vLy8iI6O5tChQxw5coSNGzcSGBhosB9zkyZNjJYB17t58ya7d+/G0dGRZs2aGZzbuHEjzz//PJmZmTRr1oyOHTsSHR1NcHAwBw8eZPfu3eqe3no6nY5hw4apq//07duXe/fusWHDBgICAti3bx+tWrUyasfXX3/N1KlTsba2xtfXFzs7OwIDA+nXrx8LFy7M836cIn+e1gGl7733njqgtKBj3OzL5l65cqVY25lbW55kNSra8MlLZbkUmcZS/wccOZtidE3U/Uxm/3qfNbvimTjAnU5NZb9CIYQQQgghROlwJyaDb9bdN/m3DoCLo5YJ/d3o38EZKyv5O6ckKZmZQyGKwLp164CsJeKfffbZHK9NSEgAspLZJZF+lpm3t3eOAwKeeeYZozKi4Dk7ahnW3ZUhXV04di6F3/c/JPSC6V+QV2+lM2/1fX7a/IA+7ZwY2NmFCmXkv2YhhHiS9enThz59+lCrVi2D41FRUfj6+hIUFMTixYsNktODBg1i0KBBJut799132b17N35+fjg7O6vHMzIy+Pe//01mZiarVq1i5MiR6rkLFy7QoUMHVq9ezUsvvWSQwFuyZAmbNm2iTp06BAcHU758eSArcT906FBGjRrFhQsXDGKOsLAw3n77bezs7Ni3b586s/fixYu0a9eOqVOn0rt3b7y9vfP/xgmLyIDSgh9Qmpr6z1ZBpmadF2U7c2vL06BOVVv+79/lOH8tlaX+cZz42zjOvnE3g5k/36NVfXtef8GTSmVL5ve4EEIIIYQQQmRkKqzf85AVAXGkphvPZNdooF8HZ17s74ab89P5d2BJJ9MoxVMpMjKSy5cvA9C0aVPOnj2b40dMTAyAutRmbixZ5jW3D0v3zUxJSeHevXsAVKlSJcdrPTw81AEDkZGRFtUv8k+r1dCmkQNfvVaOZR9XZGBnZxzsTI84e5ikY93uh4z++BYf/RDNyTBZYl4IIZ5UtWrVMkq0A5QvX553330XgL1791pUl6IorFmzBoAxY8YYnPv777+5e/cudevWNUi0A9SrV4/Ro0cDWcvMZ6ff3/urr75SE+0AQ4YMYcCAAVy+fJktW7YYlFmwYAGZmZlMmjTJYAltHx8fPvjgAzIyMliwYIFFr0k8HhlQWvADSoOCgtTP9UvBZ1eU7cytLU+T+jXtmP16Oea9WY6Gte1MXnPsfAovfnab1TvjSM+Q2FoIIYQQQghRsvx1OYVX/u8OP21+YDLR3qi2HT/8pwJTR3hKor0Ek+Hd4qmk31MRsvY6tHS/w5I4G+vhw4fq59lns5nj5OREYmKi2rkqika1Cja88bwnLw1wZ9eRRDYHPeTGXdNLzB86k8yhM8nUqGjDCz1c6NbSCWtZFkYIIZ4KNjY2AAZLyOdk//79REZGUqFCBXx9fQ3O2dmZTj49qkyZMurn165d48KFCzg4ONC3b1+ja4cOHcrWrVvx9/dnyJAh6nH9vuxDhw41WWbatGn4+/uzcOFCi9ok8sfUgNKc5GdA6eNaunQp48ePz/W6/AwoTUxMLPABpTqdjv/+97/q18OHDy+2dubWlqdVEx97Fkyz4/j5FJb4x3ExIs3gfFq6ws9b4/jjeBLTRnjQyNu+mFoqhBBCCCGEEFniEjL5afMDtockmjzv4apl8mAPurd0lK2xSgFJtoun0unTp/NVrmnTphZd99dff+Wr/uxy66zTS0n5Z9lESzrm9R3vycnJ+WuYeCxODloGd3VhUGdnQi9kLTF/7JzpJeav307nvyvuszwgjhd6utKrjTO2NvKLVQghnlSxsbHMnTsXwGSi25SVK1cCMGLECKMlpWvVqkXt2rUJCwtj9erVRsvIr1y5Eg8PD/z8/NTjf/75JwANGzZUE//Z6feEP3PmjHrswYMHREREAKZjpapVq1K2bFnCw8OJj4/PcQ9x8XhkQGnBDyidP38+x44dA2Dw4MFGy8QXZTtza8vTTKPR0KqBAy3r27P/RBLfb3xATFymwTXht9N5Y95d+rRz4uVB7jIrRAghhBBCCFHkFEUh8GgiP/z+gLgEndF5jQYGdHRm4gB3nB1lcfLSQpLt4ql06dIlIKuzK7eOro8//pjPPvsMgE6dOllUf8OGDR+vgXlgb//PzIy0tLQcrsyi3+fRwcGh0NokcqfVZnUItmrgQGRUOluCHrLzSCJJKcZLxdyOyWT+mlh+3RHP874u9O3gjL2t/KIVQojS7tKlS8yaNQudTkdUVBQhISEkJCQwadIkRo0alWv5lJQUNm7cCBgvIQ9Z+zkvX76cfv36MWrUKObOnUudOnW4e/cuwcHB1K9fn2XLluHp6amW0SfNzQ360x8PDw83KpN9uxpT5e7du0d4eDiNGjUy+5pSU1MN9qSOj483e60wJgNKC3ZAaVBQEP/5z38AKFeuHIsWLTK6pqjaaUlbRFbSvWsLJ1o2cGDJ1gdsOZDAozszbQ9J5NCZZCYPdqdHayeZJSKEEEIIIYQoEuG30/l67X3+vJRq8rx3FRumjvSkXg3LVioUJYck28VTSd9BXK1atVyv/eOPP4CsPUcrV65cqO3KDxcXF/VzS2bIJCZmLUtiycwbUTSqlrdhynBPXhzgTuDRRDbtf0hklPES8/ceZPLdhges2hnP0O4uDOzkgpODJN2FEKK0ioqKYvny5QbHXn/9dT777DO02tz/f9+6dStxcXE0aNDAbLK0ffv2BAUF4efnx8mTJzl58iSQlRTs0aOH0d7x+ljC0dHRZH36ZHr22by5lTFXzpQvvviCmTNn5niNME8GlBbcgNJz587h5+dHRkYG9vb2rF+/nnLlyhVLOy1ti/iHs4OW15/3pGdrJ+atuc/lyHSD83EJOv674j47Dyfy5ghPqlUwXslDCCGEEEIIIQpCSpqOVTviWbc7noxM4/MOdhom9HfDr7MLVrKdbKkkyXbxVEpKSgIM9yg15ebNmxw9ehSA559/3uL6c9sf0xJVqlTB3d091+vs7e0pU6YMMTEx3LhxI8drY2Nj1WR71apVH7uNomA52msZ1NmFAR2dOXouhVU74zh/zbjT9kGCjp+3xLE2MJ7BXV0Y3NUFVydZBlMIIYqKn58fFy5cyFOZFStW0KpVK4NjHTp0QFEUMjMziYiIYNOmTcycOZMdO3YQGBiY6z7a+iXkTc1q11uzZg0TJkygTZs2rFmzhgYNGnDr1i3mzJnD3Llz2bdvHyEhIRbv717Y3nvvPaZNm6Z+HR8fLzFLHsiA0oIZUHrt2jV69uxJbGwsVlZWrF271uyAhMJuZ17aIow9U8OORdMrsCnoIUv940hONZzmfvpSKi//321e6OHKqN5usmWTEEIIIYQQokAdO5fMgrX3uR1jIssOdGrqwKtDPfDykHRtaSZPTzyVMjKyZg3n1rG8cuVKFEVBo9EwevRoi+vPaXlUSy1dupTx48dbdG39+vUJDg7m8uXLZGRkYG1t+kf777//Vj+vV6/eY7dRFA6tVkPbRg60aWjPqYuprNwRx+mLxkvLJCQrrNgez/o9DxnYyZmh3V3xdJWkuxBCFLZr164RFhaWpzL6gX6mWFlZUbNmTaZNm0aNGjUYMmQIr732Gv7+/mbLxMTEsHPnTrRardkl5y9dusS4ceMoV64c27ZtU5N7derU4ccff+TWrVts27aNJUuWMHnyZOCfBKC59uoThdkTjLmVMVfOFDs7uxKT+C+NZEDp4w8ovXXrFr6+vty6dQuNRsOSJUsYOHBgsbQzr20RpllZaRjazZXOTR1Z+FssB/80XMI/PQN+3RHP3tAk3hzhSfNn7M3UJIQQQgghhBCWufcgg+82PCDopOm+kgplrHh9uCdtGsl2v08CSbaLp1L58uW5cuVKjvuApqSk8M033wAwdOhQfHx8iqp5edahQweCg4NJTEzkxIkTtG7d2uR1QUFB6uft27cvquaJfNJoNDSra0+zuvacvZLKyp1xHDuXYnRdcqrC2j8e8vv+BPq2d+J5X1fKecp/70IIUVjyuy+2Jfz8/HB2dmbnzp2kpaWZ3QN63bp1pKen07VrV7N7YK9du5b09HR69+5tchbt8OHD2bZtGwcOHFCT7foZ0eaShvrj1atXV4/py+gTiab2bTdVThQ8GVD6eANK7927R48ePbh69SoACxcuZOzYscXSzvy2RZjn5WHNp//yIuRMEt/8Fsvd+4YzS25GZ/DON3fp3tKRyUM8ZBCrEEIIIYQQIs8ydQpbghJY4v+ApBTF6LyVFob7ujKmjyv2trJF7JNCnqR4KtWpUwfI2v/Q3N6Kn3/+Obdu3cLW1jbPe4cqivLYH5Z2QgIMGjRI/Xzp0qUmr9HpdKxYsQIAd3d3unbtmqfXJIpXw9p2/PfVcvzwnwp0bGJ6tFtausKm/QmM/uQWc1bFcDM63eR1QgghSi6NRoOnpycZGRnExsaavc6SJeT1CW43NzeT5/XHs9+ncePGQNYM5vR0498j+j3fn332WfWYu7u7mnA/deqUUZnIyEju3btH9erVcXV1Ndte8fjKly8P8EQNKAXUAaXmFMSA0ri4OHr16sX58+cB+O9//8urr75aLO18nLaI3LV71pGlH1VkuK8LWhM9InuOJzFu5i38gx+i0xl3jgkhhBBCCCGEKWHhqbz6VRTfro81mWhvVNuOxe9X4OVB7pJof8LI0xRPpT59+gBZS23++OOPRufXrFnDF198AcCsWbNK/JLrrVq1omPHjgD88ssvHD582OiauXPnqvvLvvHGG9jY2BRpG0XB8Klmy8xXvPjlwwr4tnREa2JbyYxM2H4okXEzbvN/y+5x/bYk3YUQorS4evUqkZGRuLq6UrZsWbPXHD58GAcHB4YMGWK2rgoVKgAQGhpq8vzx48cBDPaGr1mzJvXq1SM5OZmAgACjMhs2bACgf//+Bsf79u1rcN6SMqLgyYDS/A0oTUpKom/fvupgkg8++IB33323WNr5uG0RlnGw0zJpsAc//qcC9WsaryCSmKwwf00sr8+N4soN0z9LQgghhBBCCAGQkKzjm3X3+fdXUVyMMP77wdVJyzujPZk/tRw1K5lewVCUbpJsF0+lAQMGUKtWLQDeeustPvvsM44ePcoff/zB+PHjGTlyJDqdjnHjxvHWW28Vc2sts2DBAhwcHMjIyKBnz5588cUXHDlyhH379vGvf/2L6dOnA+Dj41NqXpMwr2YlW96fUJbln1SkTzsnrE2scqlTYPexJCZ+fpsZP0VzKVI6CoUQoiRYuHAhd+7cMToeFhbGyJEjURSFsWPHYmVleglj/az2gQMH5jhTXL+384EDB1i0aJHBuSNHjjB//nwga3ZzdtOmTQNg+vTp3L17Vz3++++/s3XrVry9vY32jX7jjTewsrLihx9+4MiRI+rxS5cuMWvWLKytrXnjjTfMtlUUDBlQanpAqUajQaPRGAws0UtLS8PPz49Dhw6pdXz++efF0s6CaIvIm9pVbPnmrfJMHeGBs4PxKNbz19L413/v8OPvsSSn6oqhhUIIIYQQQoiSSlEU9p9IZMKnt9kclIBiYmGsXm2cWP5JRZ5r54zW1Mw58UTQKIqpxy9E0YqPj8fNzY24uLgiW170wIED9O3bl4SEBKNzWq2WadOm8eWXX6I1tbZgCeXv78/o0aPNLh3q4+NDQEAA3t7eRdwyUdii7mew7o94Ag4lkJ5h/rrWDewZ/ZwbDWrlvJerEEI8yYoj7siuRo0aREZG0rhxY7y9vVEUhfDwcE6cOIFOp6NTp04EBASY3GcdoG7duly8eJGAgAA1uWrOO++8w5w5cwBo0KAB9evX59atWxw+fBidTscrr7xilJTV6XQMHTqUTZs24eHhQffu3bl37x5BQUHY29uzb98+WrdubXSv+fPnM23aNKytrenRowe2trYEBgaSnJzMN998w2uvvZbn96q4n1Vpk5KSQoMGDbh69So2NjZ89NFH9OzZk/j4eFatWsXy5csBGDduHEuXLkWjKfl/6J86dYr27duTnJyMs7Mz77//Pl27diU5OZm1a9eyePFiICvODQ0NxcXFxagO/eusXr06169fNzg3ZMgQfv/9dwC6devG119/neP7Ymtra3Lp/YJoZ0G1ReTP/fhMFm2MZc/xJJPny3la8fpwD9o961jELRNCFAWJOUoPeVZCCCFKgpvR6SxcF8ux8ykmz1evYM2bL3jS2Me+iFsmCpKlcYck20WJUFyB8tmzZ/m///s/Dhw4wL1796hYsSJdunTh1VdfpUWLFkXWjoIUHh7OggULCAgI4MaNG9ja2uLt7c2wYcOYMmUKjo7SOfQkux+Xyfo98WwJTiAl1fx/70187Bj9nBtNfexKRUe7EEIUpOLuoFu1ahXbt28nNDSUO3fukJycjKenJ02aNGHEiBGMGTPG7GC/Y8eO0bp1a8qVK8fNmzextrbO9X6bNm3ihx9+4MSJE8TFxeHi4kKTJk14+eWXGTFihMkymZmZLFiwgCVLlnDlyhWcnJzo2rUrM2fOpH79+mbv5e/vz+zZs9W925s2bcr06dPp16+fBe+MseJ+VqWRDCg1llOyPa9xkKk6CrqdBdEWkX+hF5JZsDaWm9GmR7B2aOzAa8M98PLI/f9fIUTpITFH6SHPSgghRHFKS1f4bXc8K3fGk5Zu3P9ua6Nh7HOuDPN1xcZa+t1LO0m2i1JFAmUhClZcQia/73vI7/sfkphs/r/5BrVseXmQO896ywg7IcTTQ+KO0kOeVf7IgFJDRZVsL6h2FlRbRP6lpSus2hnHmsB4MjKNzzvYaZjQ3w2/zi5YWUkHmhBPAok5Sg95VkIIIYrL2SupzF0VQ/gd0wNzW9W35/UXPKlUVgbmPikk2S5KFQmUhSgcCck6tgY9ZP3eh8QlmN9nsm0jB14e5E6Nisb7hwohxJNG4o7SQ56VEKI4RdxJ5+s19zl9KdXk+Ua17Xh/QhnKe0pnmhClncQcpYc8KyGEEEUtOUXHz1sfmN2XvYybFVOGedCpqYOsIvuEkWS7KFUkUBaicCWn6th2MIHfdj8kJs7E9BxAq4FebZwY389NlsUUQjzRJO4oPeRZCSGKm6Io/HE0kUW/PzA5eNXFUcs7oz3p0ES26xKiNJOYo/SQZyWEEKIoHTuXzLw197l737hPXauBQZ2dmdDfHSeH0rNVm7CcJNtFqSKBshBFIy1dYefhBNb+Ec+dGNNJd1sbDYO7ujCypyvOjhIkCCGePBJ3lB7yrIQQJUVcQiY/bX7A9pBEk+cHdXZm0mAPbG1kJosQpZHEHKWHPCshhBBFIS4hk+83xPLHsSST5+tUteGtUWXwqWZbxC0TRUmS7aJUkUBZiKKVnqGw7WACv26P44GZ5eVdnbSM6u3KwE4u0mkohHiiSNxResizEkKUNGcup/Df5TEmB67WrmLDRy+WpVoF2ZpJiNJGYo7SQ56VEEKIwqQoCvtPJLHwt1iT/ea2NhrG93NjWDcXrKykz/xJZ2ncIVMWhRDiKWRjrcGviwu/zqzE2D6u2NsZBwbxiToWbXzA2Jm3CDyaSKZOxmYJIYQQQoin27Pe9ix+ryKdmxkvG3/lRjqT/nuHHYcTkHkNQgghhBBClC7RDzL46Md7fLYkxmSivUkdO375oAIv9HCVRLswIDPbRYkgo1KFKF734zJZsT2ObYcS0Jme6E6tyja8MsidlvXt0WgkmBBClF4Sd5Qe8qyEECWVoigEHErk2/WxpKUbd6t0b+nImy94yt6NQpQSEnOUHvKshBBCFDSdTmF7SCI//h5LYopxbO9kr+Ffgz3o084JrVb6xZ8mlsYd1kXYJiGEECWUp5sVb47wZEg3F37Z+oADp5KNrrl6M53/fBdNEx87/uXnTt3qdsXQUiGEEEIIIYqfRqOhXwdnGtSy5bNfYrh+O93g/J7jSVy4nsZHL5aRuFkIIYQQQogS6sbddOauus+fl1JNnm//rAOvv+CBl7ukU4V5MrNdlAgyKlWIkuXCtVR+3PSAM5dNBxkAXZo5MnGAG5XLyZ6UQojSReKO0kOelRCiNEhJ07FowwP8DyYYnbO2gpcHuTOkq4vMghGiBJOYo/SQZyWEEKIgZGYqrN/zkGUBcSZXqvJw0fLa8550buogq7w+xWRmuxBCiHyrV9OO+VPLcfRsCj9tecC1W+lG1+w/mUTw6ST6d3RmTB83PFysiqGlQgghhBBCFC97Wy1TR3rS9Bl75q6KITH5n866jExYtPEBJ/5O4T9jy+AuMbMQQgghhBDF6nJkGrNXxnAp0rjPG6BXGycmDXbHzVlid2EZSbYLIYQwSaPR0KaRAy0b2PPH0USW+scR/SDT4JpMHWwOSmDXkUSe7+HKsG4uOPw/e3ceZ2P5/3H8dWYfs+9jHcuQSCKJsof6JiRDlkLalHVGknaVZBmMIdpEKimypfBNZCkkO/O1r2X2zezb+f3hN4fjzDDWM2Pez8fjPBrXdX/u+zrnhMv9ua/P5aR9KUVERESk/GnTuAJ1gxz4YE48B47nmPVt25/F8x9GM2aAD43vcLLSCEVEREREyq+cXCPzf05hwX9TKSiw7A/wtiWsjzf31XO+9YOTMk0ZERERuSxbGwOPNHflq3cr8kI3T1ydLcvmZGYbmftTCk+9+y/LNpwjL187lIiIiIhI+RPoY8e0sAB6d3Tn0mqTCSn5jJoeyxfLk8nXfFlERERE5JbZeySL5z88yzerLRPtBgM80caVOW9WVKJdrolWtouISIk4OtjQq4M7jz7gwoLVqfy4/hy5eebHJKUWEPFdEot/O8ezXTxppT1tRERERKScsbM18PzjnjS6w5Hx8xJISr1wN89ohG9WpbL7cDZvPONDgLduy4iIiIiI3CzpmQV8viyZZRvSiuwPCrTjlad8qF/T8RaPTG4nWtkuIiJXxd3Flhef8OKrdyvxcDMXixU7AGdi8xj7eTyDJ8Ww72j2rR+kiIiIiIiVNbnTmc9er8h99SzLxu87ms3z486ycVeGFUYmIqXRnj17GDJkCM2aNaNSpUo4Ojri4eFB8+bNiYyMJDfXcl/Zd999F4PBcMXXhg0bzOLatGlz2eNXrVp11dd67bXXin1vmzdv5tFHH8Xb2xtXV1eaNm3KV199df0fmoiIyGVs2ZvJwPfPFplot7WBfo+688mYikq0y3XTI9QiInJNArztGN3Phx4PufHZ0mS27s+yOOZ/J3IYFh7Dw81ceP5xT7zdba0wUhERERER6/B2t2X8y358v/YcXyxLJv+ikpVpmUbe+TSerq1cGfSEJ44OWg8hUp5t2LCBmTNnEhQURL169fDz8yMuLo7NmzezZcsWFi9ezJo1a3BwcDDF3HPPPfTv37/I8/3zzz/8+uuvVKhQgcaNGxd5TPfu3XF1dbVor1y5crHjfPDBBwkODrZov/fee4s8fvHixTz55JMUFBTQqlUrfH19Wbt2Lf3792fPnj1Mnjy52GuJiIhci+Rz+cxclMTav4p+sPWOIAdGPeVNzcoORfaLXC0l20VE5LrUrOzA+MH+7DqUxadLk/nfiRyLY1ZvSWfT7gwGdvakS0tXbG1VWl5EREREygcbGwO9OrjTMNiRD+bEczYh36x/2YY09h7J5q1nfQmqaG+lUYqItT366KM8+uij1KxZ06w9JiaG9u3b8/vvv/Ppp58yZMgQU9/jjz/O448/XuT5Ro8eza+//kq3bt2KTKgDTJ48merVq1/VOJ977jkGDBhQomMTExMZOHAg+fn5LF68mCeeeAI4/55atGhBeHg4jz32GG3atLmqMYiIiBTFaDTy2/YMZvyQREpagUW/o72BgV08eKKtG7Y2uj8tN44em77JrqUEVKHvv/+edu3a4eXlhb29PQEBAXTt2pX169cXG5OUlMTw4cMJCgrC0dGRoKAgRowYQXJycrEx+fn5TJ06lQYNGuDs7Iyfnx89e/YkKirqsu9txYoVtG7dGnd3d9zd3WnTpg0rV6680kciIrepe+o4MXNUAO8850sVf8tnudIzjUR+n8RLE6LZf0yl5UVERESkfLmzhiOfvF6RtvdWsOg79m8uL02I5pc/0jAajVYYnYhYW82aNS0S7QABAQGMHj0agN9++61E5zIajSxYsACAp59++sYN8ip9/vnnpKam0rVrV1OiHc6/p4kTJwIQHh5ureGJiMhtJCElnzdnxzPuy4QiE+2N7nDki7cq0uMhdyXa5YZTsv0mKywBFR0dTb169XjiiSe477772LVrF8OGDaNDhw7k5FiuAg0NDeXJJ59k48aNNGrUiCeeeIIqVaqwfPly2rZty6effmoREx8fT9OmTZk+fTp2dnY8/vjjuLm5ERERwf33309iYqJFTEFBAT169CAsLIwzZ87QqVMn6tevz6JFi2jSpAnbtm0r8n1NmzaNLl268Mcff/Dggw/Srl07tm3bxmOPPcaMGTOu/4MTkTLJYDDQunEF5rxVkcEhnrg4WU5cjpzJZejkGCbNTyD5XH4RZxERERERuT25Otvw5kAfXunrjaO9+Vw5K8fIpK8TGfdlAumZljcIRaT8src/X/Xi4hLyl7N+/XpOnz5NYGAg7du3v5lDu6zCRTkhISEWfZ06dcLJyYlff/2VrCzLbelERERKwmg0svavdAa+f5Y/92Za9Ls4Gxj1lDeTh/lTyVfFvuXm0P9ZN9m1lIDas2cP06ZNw9PTk82bN1OvXj1T33fffUefPn0ICwujT58+ZmWgRowYwZEjR3jiiSdYuHAhdnbnv95hw4YRGRlJWFgYc+fONRvHnDlzWLJkCbVr12bjxo0EBAQA5/dTCgkJoW/fvkRFRZnOBXDw4EFeeeUVHB0dWbduHc2bNwfg0KFDPPDAA4SGhvLII48UuX+TiJQPdrYGurdzp829LnyyJIlft1nuj/PLn+ls2p3Js1086NTCVU8UioiIiEi5YDAYePRBV+rVdOT9L+I5/q95xbvftmcQdSKHtwb6ULe6o5VGKSKlRVJSkmn1d6dOnUoU8/XXXwPQu3dvbG1tiz3uiy++ICEhARsbG+rUqcPjjz9OtWrVLnvu3377jV27dpGVlUWVKlX4z3/+U+x+7bt37wYocs94BwcH7rrrLrZv386hQ4e4++67S/TeRERECiWm5jNtQSKbdlsm2QFa3uPMsCe98fEo/u9CkRtBK9tvsmspAbVhwwYAnnzySbNEO0CvXr1o0KAB6enpHDhwwNR+9uxZFixYgIODAx9//LFZcnzSpEn4+fnx9ddfExsba3a+KVOmADBx4kRToh2ge/fudOnShSNHjrBs2TKzmIiICPLz8xk0aJAp0Q5Qp04d3njjDfLy8oiIiLjyhyMitz0fD1teH+DL1BH+VC9i/8lzGQVM+y6JwRNjiDqh0vIiIiIiUn5Ur2jPx68G0KWl5V7KZ+PzGBYewy9/pllhZCJiTYcPH2bAgAH069ePhx9+mGrVqvHXX38xaNAg+vbte8X4rKwsFi9eDFy5hPwHH3zArFmzmDlzJsOHDyc4OJj333//sjHz588nIiKCTz75hLfeeosmTZoQEhJCWpr5n1epqamkpKQAUKVKlSLPVdh+8uTJK74vERGRi63/+/xq9qIS7V5uNrz7vC9jX/BTol1uCSXbrai4ElCOjiV7ct3Hx8f086pVqygoKKBly5ZmSfPC83Xu3Jn8/Hx+/vlnU/vx48eJiorC2dm5yCdjC0s8rVixwqz9ciWgiosRkfKtYR0nPn09kJe6e+LsaLmC/dCpHIZMiiH8mwRS0lRaXkRERETKB0cHG0b09ubd531xdTafJ+flw6T5iXy+LJmCAu3jLlJexMTEMG/ePObPn8+aNWtIS0tj2LBhTJgwARubK9/KXb58OSkpKdSvX59GjRoVeUyrVq2YP38+R48eJSMjg4MHDzJu3Djs7Ox4++23i1xEExwczOTJk9m/fz9paWmcPn2ab775hsqVK7N48WKLxP7FyfcKFSoUOQ4XFxcAzp07V+z7yc7OJjU11ewlIiLlV/K5fMZ+Hs97XySQmm659VLbJue3OG3VqOi/e0RuBpWRt5LLlYBq27YtdnZ2LFy4kGHDhlmUkd+7dy+tW7emVq1apvbLlWUqbJ8zZw579uyxiLnrrrtMif9LYwCzmOTkZE6dOgVQ5IS9atWq+Pr6cvLkSVJTU3F3d7/MpyAi5YmdrYEeD7nT9t4KzPoxmXXbzUvLG42wcnM6G3dl8lxXTx59wAUblZYXERERkXKgVaMK1KnmwLgv49l/LMes79vVqfwbl8foft44OmjNhEhp1a1bN6Kioq4q5quvvqJp06ZmbS1atMBoNJKfn8+pU6dYsmQJY8eO5ZdffmHNmjVUr179sucsLCF/uVXt7733ntmv69Spw+uvv06TJk14+OGHeffdd3nhhRdwdnY2HfPUU0+Zxbi4uNCnTx/atm1LgwYNWLp0KVu2bKFZs2YleeslNn78eMaOHXtDzykiImXThp0ZTFuQSHKaZZLdw9WGEb28ad1YSXa59ZRsv0UOHz7MuHHjKCgoICYmhj/++IO0tLQiS0AFBwczdepUhg8fTsOGDWnZsiX+/v4cPnyYnTt30rlzZ+bMmWMWU5gAv5qyTNcT4+XlZXr6tKi4+Ph4Tp48SYMGDYo8Jjs7m+zsCyWj9VSqSPnh62nHWwN96fRgFtO/S+RUTJ5Zf2p6AVO+TeTnP9IY0cubOtUcijmTiIiIiMjtI9DHjmmhAXy6NJkf1pqv8ly/I4PYpDzeH+SHl5tKYYqURsePH+fgwYNXFZORkVFsn62tLTVq1CAsLIzq1avTvXt3hg4detlqkgkJCaxatQobG5sSlZy/VMeOHWnSpAnbt29n69attGnT5ooxFStW5JlnnmHy5MmsWrXKlGx3db2wRUZGRkaRC3LS09MBcHNzK/b8Y8aMISwszPTr1NRUqlatWtK3JCIit4GUtHwiv0/it+1F/73ZqpEzw3t5a54sVmOVZLut7a35H95gMJCXl3flA2+BwhJQFxs2bBjvv/9+kSWghgwZgp+fHwMHDmTdunWm9ooVK9KhQwe8vb3Nji8szXQ1ZZluRkxxcZfSU6ki0vgOJz57oyKLfzvHV7+kkJVtXhrzfydyeGlCNJ1buDKwiwfuLposiYiIiMjtzdbWwEvdvajib0fEwiQKLlq0c+B4DoMnRvPhy/5Ur2hZnU5ErGvXrl037dzdunXD1dWVVatWkZOTY7ElZaGFCxeSm5tL27Zti11ccyW1a9dm+/btnD179qpiALMYd3d3PDw8SElJ4cyZM2aVOwudOXMGgKCgoGLP7ejoWOItN0VE5PazeU8GU75NJCnVcjW7u4sNw570ou29FTAYVCFVrMcq9ceMRqPpvzf7db26detG3bp1r+q1bds2i/MUloDKy8vj2LFjhIeHM3fuXJo0acKJEycsPp8RI0bQq1cv+vXrx6FDh0hLS2Pr1q0EBwczdOhQhgwZct3vzZrGjBlDSkqK6XX69GlrD0lErMDezkCvju7MfatikSV+jEZYvjGN/mPP8sufadqrUkRERETKhc4t3Rj/sh8uTuY3DaMT8hk6OZq//5dlpZGJiDUYDAa8vb3Jy8sjKSmp2ONKUkL+SgrPX1xFy6uJadiwIQA7duywiMnNzWXfvn04OTlRp06dax2uiIjcps5lFPDRvATemh1fZKL9wbudmfNmRdo1cVGiXazOamXkS5oIL/xNcqXjL/7NdCOS7IWsUQJq3rx5RERE0LVrV2bNmmVqb9q0KStXrqRu3brMmjWLl19+mfr16wMXSjMVd+2iyjLdjJji4i6lp1JF5GL+3na885wvfx3IJPL7JM7EmlclSUkrYNL8RFZuOl9aPriqSsuLiIiIyO3tvnrOTH8lgNc/jiMmMd/Unp5p5LUZsYzo7U2nB10vcwYRuV0cO3aM06dP4+7ujq+vb7HH/Pnnnzg7O9O9e/druk5cXBwbN24EoHHjxiWKMRqNLFmypMiYTp06sWHDBhYtWmSx5/tPP/1EVlYWjz32GE5OTtc0XhERuT1t2ZdJ+DeJJKTkW/S5OhsY2tOb9k21ml1KD6usbD9+/PgVXz/++CO+vr4YjUbs7Ozo3LkzH3/8Mb///jv79u3j8OHD7Nixg8WLFzN69Ghq1KhhSrL36tWLw4cPc+zYsese665du656NX1J9jMCyxJQhebPnw9ASEiIRYybmxuPPPIIRqORTZs2mdqrVasGXCi/dKmiyjJdT0xSUpIpqV6SOBGRkrivnjOfv1GR57p44GhvOVk6cDyHQR9FE/l9ImkZlk80ioiIiIjcTmpUcmDmqEDuCDJ/2DS/AMK/SeTTJUmq/iRym4iMjCQ6Otqi/eDBg/Tp0wej0Ui/fv2K3Z6zcFV7165di9wfvdAff/zB0qVLyc83T2CcOHGCbt26kZ6eTpcuXczK0MfFxTFz5kyLLSPT0tJ46aWX2Lp1K4GBgTzxxBNm/c899xzu7u4sW7aMH3/80dQeGxvLq6++CsDIkSOLHauIiJQvaZkFTJqfwOsfxxWZaG92lxNz3qpIh/u1ml1KF6usbL9SEnbt2rX07t2bnJwc2rZty2effUbNmjWLPPaee+6hW7dufPjhh3z++eeMGDGChQsXEh8fz6pVq27G8G+YwhJQp06dIikpiYCAAOBCstrDw6PIuML2i8tGXa4s08Xtd999t0XMvn37yM3Nxd7e/ooxnp6eVKtWjVOnTrFz505atGhhFnP69Gni4+MJCgq67MReRKQ4DvYG+jziQbv7XPh4URKbdmea9RcYYcn6NNb/ncGL3Tw1uRIRERGR25q3hy1TQ/35aF4CG3aaz42/++85/o3P47X+Pjg5WGU9hYjcIOHh4YwYMYKGDRsSHByM0Wjk5MmT/P333xQUFNCqVSvGjx9fbPw333wDXLmE/KFDh3jmmWcIDAykcePGeHp6mq6TlZVF/fr1+eyzz8xi0tPTGTJkCK+99hr33XcfFStWJC4ujh07dpCQkICnpyeLFi2iQgXz7eG8vb2ZM2cOPXv2JCQkhDZt2uDj48Ovv/5KcnIyYWFhJV60JCIit7e/DmQy+etE4pItk+wuTgYG9/Di4Wa6DyylU6n7l1hMTAy9evUiJyeHDh06sHr16mIT7RczGAw8//zzrFy5EhsbG9auXcsbb7xxC0Z87YorARUYGAjA9u3bi4wrbK9evbqp7ZFHHsHGxoaNGzcSGxtrdnx2djYrVqzA1taWRx991NReo0YN7rzzTjIzM1m5cqXFdRYtWgRA586dzdo7depk1l+SGBGRqxXoY8d7L/oxfrAflfwsnw1LOlfAR18lMmJqLKeic60wQhERERGRW8PJwYa3n/WlV0fLh9o37MwkbFosiUWs/hGRsmPcuHH06tWL9PR0Vq9ezbJlyzh16hQdOnRg7ty5rFu3zrS946W2bdvGoUOH8Pf3p2PHjpe9zv33389LL71EpUqV+Ouvv/j+++/Zt28f99xzD+Hh4fz111/4+/ubxfj4+DB69GjuvfdeDh06xOLFi9m8eTOBgYGMHDmSffv28eCDDxZ5ve7du7NhwwYefvhhdu7cyc8//0xwcDBz584lPDz82j4sERG5bWRkFTDl20RGz4grMtF+X73zq9kfae6qRLuUWgbjjdzg/AYYN24cb731FjY2Nhw5csQsoVxSAwcOZO7cubi5uXH27FmLpypvpcjISHr06GFKoBc6ePAg/fv3Z+vWrQwZMoTIyEhT39SpUwkLC8PV1ZW1a9fStGlTU9+MGTMYOnQobm5unDhxAm9vb1PfU089xTfffEP37t357rvvsLM7n5waPnw406dPp3///sydO9dsHJ9//jnPP/88tWvXZtOmTabJ9I8//kj37t0JDg4mKirKdK7CsdevXx87OzvWr19Ps2bNADh8+DDNmzcnJSWFqKgogoODS/w5paam4uHhQUpKilbEi4iFnFwjC/+byjerU8nJtfxry94OnunsSY+H3LC10aRLRC5P846yQ9+ViIillZvTmLYgkfxLdlUK8Lblw5f9qFHJoehAESmW5hxlh74rEZHbx47/ZTHp6wRiEi2T7BWcDLzU3YtHH9BqdrGeks47Sl2y/b777uPvv//mrrvuYs+ePdd0jh9//JGQkBAMBgPLli3jscceu8GjLLnq1atz+vTpy5aAWrlypdmTqVlZWXTo0IFNmzZhY2ND8+bNqVSpEvv37+fAgQPY2try5ZdfWpSFio+Pp1mzZhw9epRatWrRpEkT9u/fz759+6hduzZbtmwxS84DFBQUEBISwpIlS/Dy8uKhhx4iPj6e33//HScnJ9atW8f9999v8b4KHwiws7OjQ4cOODg4sGbNGjIzM5k+fTpDhw69qs9JE2URKYmz8XnMXJTEH3syi+yvV8OBV/v5UC3Avsh+ERHQvKMs0XclIlK0v/+XxbufxZGeaX5Lx8XJwNvP+XJfPWcrjUykbNKco+zQdyUiUvZlZhXw6dJklm1IK7K/8R2OvPKUD4E+VtkJW8SkpPOOUldG/uTJkxgMBvz8/K75HBfHnjp16kYM65pdSwkoJycn1q5dS3h4OE2aNGHPnj0sWbKEpKQkQkJC2LRpU5H7L/n6+rJt2zaGDh1KTk4OS5YsISUlhWHDhrFt2zaLRDuAjY0NP/zwA+Hh4VSqVImffvqJvXv30r17d7Zv315koh0gNDSU5cuX07x5czZu3MjatWtp0qQJK1asuOpEu4hISVX0teODQX6Me8mPij62Fv0HjufwwofRfP9rKvkFpepZMhERERGRG+beuk5EvhJI4CVz4vQsI2M+jmPFxnNWGpmIiIiISPF2H8riuXFni0y0OzkaGNHLi0nD/JVolzKl1K1sd3FxISsri1q1anHo0KFrOseCBQvo27cvBoOBjz76iFGjRt3gUcqNpqdSReRqZeUUMGd5CovXnaOov8nq13Tg1ad9qKpV7iJyCc07yg59VyIil5d0Lp+3Zsdx4HiORV+Ph9x4oZuntlkSKQHNOcoOfVciImVTVk4Bny9N5sf1Ra9mv6e2I6Oe9qGir5LsUnqU2ZXtlStXxmg0cvToUXbt2nVN5/juu+/MziciIrcfJwcbXg7xYlqoP5X9LCdh+4/l8PyH0fywVqvcRUREROT25OVmS/hwf9o0rmDR98Pac7z7WTyZ2QVFRIqIiIiI3BoHT2YzaHx0kYl2JwcDQ3t6MXm4vxLtUmaVumR7+/btTT8PHDiQlJSUq4r/6quvWLFiBXC+RHrbtm1v6PhERKR0aRDsxGdvBPJEWzcMlyzayck1MmtxMqFTYzkdk2udAYqIiIiI3ESODja8OdCHvg9brrTYvDuT0KmxJKTkW2FkIiIiIlKe5ecbmf9LCkMmxXAqJs+iv0EtRz57PZBubdywUTUmKcNKXbL9xRdfxNb2/J5ju3fvplmzZqxdu/aKcampqYwaNYqBAwcCYDAY6Nq1KxUrVryp4xUREetzcrBhSA8vpob6U6mIVe77jmZrlbuIiIiI3LZsbAw829WTUU97Y3vJnZ5Dp3IYPDGao2csS82LiIiIiNwM/8TmMnxKDF+uSCH/kkJLDvYGXg7xZGqoP5X9tQWolH2lbs92gHfeeYf3338fg8GA0WjEYDBQu3ZtOnbsyF133YWPjw8ODg6cO3eOEydOsG3bNv773/+SlZVF4dvx9/dn165dBAYGWvndSElovyURuVEyswv4Ylnx+/80qOXIq097ayInUo5p3lF26LsSEbl6Ow9m8c6ncaRlmt/uqeBk4K1nfbm/vrOVRiZSemnOUXbouxIRKd2MRiMrN6fz8eIksrIt0493BDkwZoAP1QJ0b1ZKv5LOO0plsh3gtddeY+LEiWYJ98spPMZoNFK1alVWr15N3bp1b9Fo5XppoiwiN9ruw1lMnJ/I2XjLEkWO9gae6+qhEkUi5ZTmHWWHvisRkWtzKiaXMTPjLObCNgYY2tOLrq3drDQykdJJc46yQ9+ViEjplZiaz+SvE9iyL8uiz8YGnnrEnaf+44Gdre7HStlQ0nlHqSsjX+ijjz7iv//9L3feeSdwPpl+8evSNgB7e3sGDx7M3r17lWgXESnnGtZ24vM3AunWxtWiLzvXyMxFyYROi+WfWO3lLiK31p49exgyZAjNmjWjUqVKODo64uHhQfPmzYmMjCQ3t/g/l77//nvatWuHl5cX9vb2BAQE0LVrV9avX19sTFJSEsOHDycoKAhHR0eCgoIYMWIEycnJxcbk5+czdepUGjRogLOzM35+fvTs2ZOoqKjLvrcVK1bQunVr3N3dcXd3p02bNqxcufJKH4mIiNxg1QLsmTkqgPo1HczaC4wQsTCJmYuStL2SiIiIiNwwm3dn8OwHZ4tMtFf2syNyZAADHvNUol1uS6V2ZfvF/vjjD5YuXcpff/3FkSNHSEpKIicnB3d3d/z9/WnUqBEtW7akZ8+eeHt7W3u4cg30VKqI3Ey7DmUxaX4CZxPyLfoc7Q08/7gnj7d21Sp3kXLC2vOOGTNmMHToUIKCgggODsbPz4+4uDg2b95MVlYWrVu3Zs2aNTg4mCdIQkNDmTZtGnZ2drRs2RI/Pz+OHDnCjh07APjkk0944YUXzGLi4+Np3rw5R44coWbNmjRp0oT9+/ezf/9+6tSpw59//mkxfy4oKCAkJIQlS5bg6enJQw89RHx8PBs2bMDZ2Zl169bRtGlTi/c1bdo0QkNDsbOzo3379jg6OrJmzRoyMzOJjIxkyJAhV/1ZWfu7EhEp63JyjUycn8Bv2zMs+h6425k3Bvjg7FRq12GI3DKac5Qd+q5EREqXjKwCPl6UxM9/pBfZ37mlK4Oe8MTZUXNOKXvKfBl5KV80URaRmy0zq4BPlyWz7Pei93K/O9iRUU97U9lP+wWJ3O6sPe84duwYADVr1jRrj4mJoX379uzbt88iOb1nzx4aNmyIp6cnmzdvpl69eqa+7777jj59+lChQgWio6Nxdb1Q0eOpp57im2++4YknnmDhwoXY2dkBMGzYMCIjI+nfvz9z5841G8fnn3/O888/T+3atdm4cSMBAQEALF68mJCQEIKDg4mKijKdC+DgwYPUr18fOzs71q1bR/PmzQE4dOgQDzzwACkpKURFRREcHHxVn5W1vysRkduB0Whk3soUvvo51aLvjmoOTBjqh7uLrRVGJlJ6aM5Rdui7EhEpPfYdzWb83PgiFzh5udswqq8PzRo4W2FkIjdGmS8jLyIiciM5O9kw/Elvwof7E+hjeTNxz5Fsnh8XzY/rzlGgkpoichPVrFnTItEOEBAQwOjRowH47bffzPo2bNgAwJNPPmmWaAfo1asXDRo0ID09nQMHDpjaz549y4IFC3BwcODjjz82S45PmjQJPz8/vv76a2JjY83ON2XKFAAmTpxoSrQDdO/enS5dunDkyBGWLVtmFhMREUF+fj6DBg0yJdoB6tSpwxtvvEFeXh4RERFX/nBEROSGMxgMDHjMk9f6eWN3yTT44KkcXpkeS0qa5Q1SEREREZGi5OYZ+XxZMiOmxBSZaH+woTNfvFFRiXYpN5RsFxGRcqXRHU588UZFuray3Ms9K8fIjB+SGBkRy7/xeVYYnYiUd/b256trXFpC3tHRsUTxPj4+pp9XrVpFQUEBLVu2NEuaF56vc+fO5Ofn8/PPP5vajx8/TlRUFM7OznTq1Mni/CEhIcD5vdkvVrgve2F/SWJEROTW6tjMlUnD/HF3Mb8VdOR0rhLuIiIiIlIix//NYfDEaL5dncql65UqOBl49Wlv3nvBF083VU6S8qPMJdvT0tI4ffo0R44cIT4+nvx8/WNQRESujrOTDcN7eTO5mFXuuw9n89y4syz9XavcReTWSUpKIjw8HMAi0d22bVvs7OxYuHCh2ep1OF9Gfu/evbRu3ZpatWqZ2nfv3g1A48aNi7xeYfuePXssYu666y5T4v9KMcnJyZw6dQqARo0aWcRUrVoVX19fTp48SWqqZQljERG5dRrWdiLylQD8PM3nwEfP5DIyQgl3ERERESlaQYGRxb+lMuijaI6cybXob1DLkc9er8gjzV0xGAxWGKGI9ZT6ZHt2djZffvklXbt2JTAwEA8PD6pXr84dd9xBQEAAzs7O3HvvvYwYMYK9e/dae7giIlKGNL7Dic/fqEiXlkWscs82Mn1hEq9ExHJWq9xF5CY4fPgwAwYMoF+/fjz88MNUq1aNv/76i0GDBtG3b1+zY4ODg5k6dSqpqak0bNiQdu3a0atXL+6991769OlD586dWbx4sVlMYQK8SpUqRV6/sP3kyZM3JMbLywsXF5cSxxUlOzub1NRUs5eIiNxYVQPsmRLqj7+XecL92D+5jJwWS/I5JdxFRERE5IK4pDxejYxl5qJkci+5TWpnC88/7smUUH8q+toVfQKR21yp/j//m2++YcSIESQmJgJgNFquLszLy2PXrl3s2rWLyMhIOnfuzOzZswkMDLzVwxURkTKogpMNI3p706pRBSZ9nUBMovnNxV3/v8o9rI83D91XdBJJRORaxMTEMG/ePLO2YcOG8f7772NjY/lM7JAhQ/Dz82PgwIGsW7fO1F6xYkU6dOiAt7e32fFpaWkAVKhQocjrFybGz507d1Njiosryvjx4xk7duxljxERketX2c+eKaEBhE2LIfai+e+xf3MJmxZL+Ah/vFT6U0RERKTcW/tXOhHfJZKWaZmfq17RntcH+BBc1aGISJHyo9SubB82bBj9+vUjISEBKDrRXshoNJpey5cvp2HDhkRFRd2qoYqIyG2gcV0nvnizIp1bWK5yz8w2Mu7LBCZ/nUBWToEVRicipUm3bt2oW7fuVb22bdtmcZ4WLVpgNBrJy8vj2LFjhIeHM3fuXJo0acKJEyfMjjUajYwYMYJevXrRr18/Dh06RFpaGlu3biU4OJihQ4cyZMiQW/QJ3DxjxowhJSXF9Dp9+rS1hyQictuq5GvH1BEBBHibJ9VPnD2fcE9M1Qp3ERERkfLqXEYB78+JZ9yXCUUm2ns85Mbs1wKVaBehlK5snzBhAjNmzDDt62BjY0P79u157LHHaNCgAb6+vjg4OHDu3DmOHj3K1q1bWbhwIWfOnMFgMBAXF0eHDh3Ys2ePxQofERGR4lRwsiG0jzetGp9f5R57ySr3n/9IZ//xHN5+1ocalTSRFCmvjh8/zsGDB68qJiMjo9g+W1tbatSoQVhYGNWrV6d79+4MHTqUFStWmI6ZN28eERERdO3alVmzZpnamzZtysqVK6lbty6zZs3i5Zdfpn79+gC4urpe9trp6ekAuLm5mdpuRkxxcUVxdHTE0dHxsseIiMiNU9HXjqn/v8I9OuHC3Pfk2VxGToshfHgA3h5a4S4iIiJSnvz9vywmfJVAfLLlw5d+nraM7u9D4zucrDAykdKp1K1sP3PmDO+9954p0d60aVN27drFL7/8wuDBg2nVqhX16tUjODiYRo0aERISwqRJkzh+/DiTJk3Czs4Og8HA2bNneffdd637ZkREpEy6t64TX7xRkYebWZaNP3k2l5cnxPDz5rTLVl0RkdvXrl27zCorleTVpk2bEp27W7duuLq6smrVKnJyckzt8+fPByAkJMQixs3NjUceeQSj0cimTZtM7dWqVQPOz6+LUtgeFBR0Q2KSkpJMSfWSxImISOkQ6HM+4V7RxzypfjI6j9BpMSSkaIW7iIiISHmQnVPAjB+SGDU9tshEe/v7KvDFmxWVaBe5RKlLts+dO5fMzEwAmjVrxrp160yrcy7H1taWkSNH8v3335tuas6ZM4fs7OybPWQREbkNuTjbMLqfD2P6++DkaDDry841MvmbRMZ9mUB6psrKi8iNYzAY8Pb2Ji8vj6SkJFN7YbLaw8OjyLjC9otjGjZsCMCOHTuKjClsv/vuuy1i9u3bR25uboliPD09TQn3nTt3WsScPn2a+Ph4goKCcHd3L3IsIiJiXQHe/59w9zUvgHg6Jo/QqTHEJedZaWQiIiIiciscPp3DoAkx/LjunEWfq7OBtwb68PozvrhWKHVpRRGrK3W/K3755RfTz5999hnOzs5XFd+1a1d69OgBQGZmJr///vsNHZ+IiJQvHe534ZPXAgmuYm/R99v2DAZ9FM2hUzlFRIqIXL1jx45x+vRp3N3d8fX1NbUHBgYCsH379iLjCturV69uanvkkUewsbFh48aNxMbGmh2fnZ3NihUrsLW15dFHHzW116hRgzvvvJPMzExWrlxpcZ1FixYB0LlzZ7P2Tp06mfWXJEZEREoXf287poX6U9nPPOF+JjaPsKmxxCUp4S4iIiJyuykoMPL9r6kMnhjNybOWD93fW9eJL96qSNsmlhVAReS8UpdsP378OAaDgTp16lCvXr1rOkf37t3NziciInI9qgbYM2NUIF1bu1r0/ROXx9DJ0fy47pzKyotIiURGRhIdHW3RfvDgQfr06YPRaKRfv37Y2l4o5/v4448DMGXKFLZt22YWN2PGDDZu3IibmxsdO3Y0tVesWJHevXuTk5PDyy+/TF7ehSTJq6++SlxcHE899RT+/v5m5wsLCzMdc3GS/scff2T58uUEBwfTtWtXs5jhw4dja2vL7Nmz2bJli6n98OHDjBs3Djs7O4YPH17Sj0hERKzEz8uOKaH+VPE3T7j/E5dH6LRYYhOVcBcRERG5XSSk5DN6Rhyzf0wm75Kq8Q72Bob08GLCED/8PO2KPoGIAGAwlrLMgJOTE7m5ubRp04a1a9de0zk2btxI69atMRgMjBs3jtdee+0Gj1JutNTUVDw8PEhJSVF5UREp1TbszGDS1wmkZ1r+9flgQ2dGPeWNu4ttEZEiUlpYe95RvXp1Tp8+TcOGDQkODsZoNHLy5En+/vtvCgoKaNWqFStXrsTV9cIDPllZWXTo0IFNmzZhY2ND8+bNqVSpEvv37+fAgQPY2try5Zdf8vTTT5tdKz4+nmbNmnH06FFq1apFkyZN2L9/P/v27aN27dps2bIFb29vs5iCggJCQkJYsmQJXl5ePPTQQ8THx/P777/j5OTEunXruP/++y3e19SpUwkLC8POzo4OHTrg4ODAmjVryMzMZPr06QwdOvSqPytrf1ciIuVVfHIeIyNiOR1jnlyv6GvHlBH+BHjrhqvcXjTnKDv0XYmI3Bh/7s1k4vwEUtIst8isXdWeMQN8qV7RstKnSHlS0nlHqVvZ7unpCWBR6vJqxMXFmX4ubl9LERGRa9GqUQU+HVORO6s7WPRt3p3JC+Oj2X8s2wojE5GyYty4cfTq1Yv09HRWr17NsmXLOHXqFB06dGDu3LmsW7fOLNEO5x9IXbt2LeHh4TRp0oQ9e/awZMkSkpKSCAkJYdOmTRaJdgBfX1+2bdvG0KFDycnJYcmSJaSkpDBs2DC2bdtmkWgHsLGx4YcffiA8PJxKlSrx008/sXfvXrp378727duLTLQDhIaGsnz5cpo3b87GjRtZu3YtTZo0YcWKFdeUaBcREevx9bRjyogAqgWYJ9XPxucRNjWG6AStcBcREREpi7JzCpi+MJE3ZsVZJNoNBujV0Z0ZowKVaBe5CqVuZXvTpk3Zvn07NjY2HD16lKCgoKs+x8CBA5k7dy4Gg4Hly5eb9pCU0ktPpYpIWZOXb+SLZcks/PWcRZ+NDTzbxZMn27thY2OwwuhE5HI07yg79F2JiFhXYko+IyNiOBltnlwP9LFlyogAAn20wl1uD5pzlB36rkRErt3xf3P4YE4Cx/+13Jvd19OWMf19aHSHkxVGJlI6ldmV7YX7TBqNRgYPHnzV+99u2bKF+fPnA+Dg4ECbNm1u9BBFRESwszXw4hNejB/sh4er+V+nBQXw2dJkxnwcR9K5/GLOICIiIiJSunl72BI+IsBiZVN0Qj4jpsZwNl4r3EVERERKO6PRyNLfzzHoo+giE+0PNnTms9cDlWgXuUalLtnev39/7O3P/yPul19+oWvXrkRHR5co9scff+Q///kP+fn5GAwGevbsiYuLy80croiIlHP313fm09cDuTvY0aLvrwNZPD/uLDsOZllhZCIiIiIi18/b3ZbwEf7UqGSecI9NzCd0agz/KuEuIiIiUmoln8vnjVlxTF+YRO4l0zZHewNhfbx57wVfPFxtrTNAkdtAqUu2165dmxEjRphWtK9cuZI6derw7LPPsmjRIg4ePEh8fDypqan8888/bNq0iSlTpnDffffRo0cPUlJSgPN7tY8fP96ab0VERMoJP087wof70+9RdwyXVI1PTC1g1PRY5v6UTH5Bqdq5RURERESkRLzcbAkf7k/Nypck3JPyCZ0Swz+xliukRERERMS6tkdl8ty4s2zZZ7kQqFYVe2a/FshjLVwxXHpDU0SuSqnbsx2goKCAXr16sWjRIgwGA0ajscS/2Y1GIxUqVGDNmjU88MADN3mkcqNovyURuV3sPJjFh3MTSEixLB/fsLYjrz/jg5+n9rYUsSbNO8oOfVciIqVLSlo+r0yP5egZ8+S6r6ctU0b4U8XfvphIkdJNc46yQ9+ViMiV5eYZ+WJ5Mt//eq7I/u7t3Hi+qycO9kqyi1xOmd2zHcDGxoaFCxcyYcIEHB3Pl+UtfCbAaDRe9tW0aVO2b9+uRLuIiFhFozuc+PT1QO6rZ7nH0e7D2bzwYTRb9mVaYWQiIiIiItfHw9WWycP8Ca5inlSPT84ndGosp2O0wl1ERETEmk7H5DJkUnSRiXYvNxs+GuzH4BAvJdpFbqBSmWwHMBgMjBo1ipMnTzJu3DiaNm1q2sv9UpUqVeLJJ59k9erVbNmyhbp1697i0YqIiFzg5WbL+Jf9eOFxT2wu+Zs2Ja2A1z+OY/aPSeTmlbriMiIiIiIil+Xhasvk4f7Urmp+jyYhJZ+wabGcilbCXeRa7NmzhyFDhtCsWTMqVaqEo6MjHh4eNG/enMjISHJzLX9vvfvuuxgMhiu+NmzYYBbXpk2byx6/atUqi2uV5Drt2rUzi1m/fv1lj2/WrNmN/RBFRMoxo9HIL3+k8eL4aA6ftvw7o2l9Jz57oyJN6ztbYXQit7dSX8fWz8+PMWPGMGbMGHJycjh9+jTJyclkZ2fj4eGBn58f/v7+1h6miIiIGRsbA706utMg2JH358QTm2heVv77X8+x50g2bw30paJvqf/rWERERETExN3FlsnDAxg1PZZDp3JM7ecT7jGEDw8gqKJKyotcjQ0bNjBz5kyCgoKoV68efn5+xMXFsXnzZrZs2cLixYtZs2YNDg4Opph77rmH/v37F3m+f/75h19//ZUKFSrQuHHjIo/p3r07rq6uFu2VK1e2aCvuOgArV64kPj6eli1bFtlfq1YtWrRoUWS7iIhcv3MZBYR/k8CGnZbVNO3t4MVuXnRro73ZRW6WUrlnu5Q/2m9JRG5nqen5TPo6kc27LSe8Ls4GRj3lQ6tGFawwMpHySfOOskPflYhI6ZaWUcCoyFgOnswxa/dytyF8eADVlXCXMqI0zDmOHTsGQM2aNc3aY2JiaN++Pfv27SMyMpIhQ4aU6HyjR49m4sSJ9O3bl6+//tqsr02bNvz+++8cP36c6tWrX9e4k5OTCQwMJDs7m0OHDlG7dm1T3/r162nbti39+/dn7ty513WdQqXhuxIRKU12H85i/NwEYpPyLfqCAu14c6Avtao4FBEpIldSpvdsFxERuZ24u9jy3gu+DOnhhf0li9jTM428+1k8M75PJD9fz7+JiIiISNnhWsGGScP8ubO6+Q3cpNQCRk6L4fi/OcVEisilatasaZFoBwgICGD06NEA/PbbbyU6l9FoZMGCBQA8/fTTN26QRfjhhx/Izs6mWbNmZol2ERG5ufLyjcxZkczIabFFJto7t3Rl1muBSrSL3AJKtouIiNwCBoOBJ9q6EflKIJX9LMvG/7g+jdEzYklNt5wci4iIiIiUVq7ONkwY6k+9Gpck3M8VEDYtVgl3kRvA3v58lYiLS8hfzvr16zl9+jSBgYG0b9/+Zg7NtGr+Zif1RUTkgn/j8xgxJYavf0ml4JK1O+4uNrz3gi+hvb1xclAKUORWKBObxB49epTly5ezdetWjhw5QlJSEtnZ2Xh6euLn50fjxo1p2bIljz32GHZ2ZeItiYhIOVWnmgOfjAlk6oJE1v6VYda342A2L0+M4f0XfalRSU+dioiIiEjZ4Opsw4Qh/rw2M5b9xy4k11PSChg9I46ZowLw89L9GpFrkZSURHh4OACdOnUqUUxhArx3797Y2toWe9wXX3xBQkICNjY21KlTh8cff5xq1aqVeGynTp1i48aN2Nvb8+STTxZ73OHDhxkzZgwJCQn4+vrSokULHnnkEWxslAQSEblaa/9KZ+qCRDKyLCtkNrrDkdf6++DnqXmXyK1UqvdsP3LkCMOHD2f16tVcOkyj0YjBYDBr8/f3Z/To0YwYMeIWjlJuBO23JCLljdFo5Jc/0olYmEhunnlfBScDrw/w4YG7tY+7yM2geUfZoe9KRKRsycgq4LWZcew7mm3WXrOyPRFhAbg4K7EmpVNpmnMcPnyYcePGUVBQQExMDH/88QdpaWkMGjSImTNnXjFBnZWVRWBgICkpKezYsYNGjRpZHFO4Z/ul7O3teeutt3jrrbdKNNbx48fz+uuv06VLF5YtW2bRX7hne1EaNGjA4sWLr1h6Pjs7m+zsC3+mpKamUrVq1VLxXYmI3ErpmQVMX5jIf7dlWPTZ2sCzXTzp2d4NGxtDEdEici3K/J7t33//Pffccw+rVq2ioKAAo9Fo9gIsfh0TE8PIkSNp06YN586ds+bwRURELstgMPDog65MCw3Ax8N8pUFGlpG3Ponn29UpFg+biYiIiIiUVhWcbJgw2I/6Nc2rNB37J5d3P4snL19zW5EriYmJYd68ecyfP581a9aQlpbGsGHDmDBhQolWgi9fvpyUlBTq169fZKIdoFWrVsyfP5+jR4+SkZHBwYMHGTduHHZ2drz99ttERESUaKxXKiHv4eHBqFGj2LJlCwkJCSQkJLB27VqaNWvG3r176dixIykpKZe9xvjx4/Hw8DC9qlatWqKxiYjcTqJOZPPC+OgiE+2V/eyIfCWAXh3dlWgXsZJSubL9l19+oWvXruTl5WEwGDAajXh4eNC+fXsaNGiAr68vDg4OnDt3jqNHj7J161b+/vtvU7zBYKBFixb89ttvly2VJKVHaXqCWETkVotLzuPt2fEcPGW5n2W7JhV45SntsSRyI2neUXbouxIRKZtS0vIZOjmGM7HmJZweae7CqKe8LSoViljbjZhzdOvWjaioqKuK+eqrr2jatGmRffn5+Zw6dYolS5YwduxYAgICWLNmDdWrV7/sObt06cKKFSv46KOPGD169FWNZ82aNTz88MN4enry77//4uzsXOyxO3bs4N5778XT05Po6GgcHR1LfJ38/Hzatm3Lxo0b+fDDDxkzZkyxx2plu4iUZwUFRhb9do7PliaTX2DZ/0hzF4b28MLZSfcNRW6GMruyPSMjgxdeeMGUaPfy8uLjjz8mOjqaH374gbfffpuXX36Z5557jtDQUGbMmMFff/3FgQMHeOyxx4DzK943bdrEjBkzrPxuYM+ePQwZMoRmzZpRqVIlHB0d8fDwoHnz5kRGRpKbm1ts7Pfff0+7du3w8vLC3t6egIAAunbtyvr16y2OTU5O5ttvv6V3797UqFEDBwcH3NzcuP/++4mIiLjsdfLz85k6dSoNGjTA2dkZPz8/evbsecV/IKxYsYLWrVvj7u6Ou7s7bdq0YeXKlSX+bERE5Dw/TzumhfnT/j7LsvG/bc9gxJRY4pLyiogUERERESl9PFxt+WiIP56u5redVv2ZzvxfUq00KpGb6/jx4xw8ePCqXhkZlisUC9na2lKjRg3CwsL48ssvOXz4MEOHDr3sGBISEli1ahU2Njb07dv3qt9Dx44dadKkCcnJyWzduvWyxxauau/Ro8dVJdrh/HsrfBBg9erVlz3W0dHRdO+x8CUiUh6kpOXz5uw4Zv9omWh3cTbw5kAfXn3aR4l2kVKg1P0u/Oqrr/jnn38wGAxUqlSJP//8k0GDBl1x0la3bl2WL19umqgZjUYmTJhwK4Z8WRs2bGDmzJlER0dTr149nnjiCe677z527drFsGHD6NChAzk5lisZQ0NDefLJJ9m4cSONGjXiiSeeoEqVKixfvpy2bdvy6aefmh0/efJk+vbty8KFC/Hy8uKJJ56gadOm7N69mxEjRtCuXbsiJ/AFBQX06NGDsLAwzpw5Q6dOnahfvz6LFi2iSZMmbNu2rcj3NW3aNLp06cIff/zBgw8+SLt27di2bRuPPfZYqXjIQUSkrHF0sGHMAB9eeNyTSxf6HDqVw6AJ0ew/ll10sIiIiIhIKVPJ145xL/vhaG8+uZ37Uwqrt6RZaVQiN8+uXbsstsG80qtNmzYlOne3bt1wdXVl1apVRd5HLLRw4UJyc3Np3bo1VapUuab3UbiH+tmzZ4s9Jj8/n++++w6Ap5566qZdR0SkvNpzJIvnP4xmy74si767ajny2esVadfExQojE5GilLpk+4oVK0w/f/LJJ6aJV0mNHz+e+++/Hzi/x1FxyeJb5dFHH+Xo0aOcOHGCX3/9lQULFvDrr79y4sQJ7rrrLn7//XeLxPmePXuYNm0anp6e7N69m99++42FCxfy999/s2DBAgwGA2FhYaSlXfjHqYuLC6+++ionTpxgx44dfPfdd6xdu5a9e/dSrVo1Nm3axAcffGAxvjlz5rBkyRJq167N//73PxYtWsT69ev54YcfyMjIoG/fvuTlma+mPHjwIK+88gqOjo5s2LCBX375haVLl7Jr1y58fHwIDQ3lyJEjN+cDFRG5jRkMBnp1dGfcID9cnMxvSialFhA2LYZVf+rGpIiIiIiUDXdWd+TNZ324dPvQyV8nsuN/ljePRaRoBoMBb29v8vLySEpKKva4K+2hXhKF53dxKT6Js3btWs6ePUtQUBAtW7a8adcRESlv8guMzP8lhbCpscQn55v1GQzw1H/cmTrCn0AfOyuNUESKUuqS7fv37wegYsWKPProo9d0joEDB1qcz1pq1qxJzZo1LdoDAgJMq/B/++03s74NGzYA8OSTT1KvXj2zvl69etGgQQPS09M5cOCAqX3MmDFMmDCBatWqmR1fu3ZtPvroIwAWLFhgMY4pU6YAMHHiRAICAkzt3bt3p0uXLhw5coRly5aZxURERJCfn8+gQYNo3ry5qb1OnTq88cYb5OXlERERUcwnIiIiV9KsgTMzRgVS2c984pybBxPnJ/LxoiTy841WGp2IiIiISMk9eHcFhvT0MmvLL4B3Po3j2D/Fr9AVkQuOHTvG6dOncXd3x9fXt9hj/vzzT5ydnenevfs1XScuLo6NGzcC0Lhx42KPK0zqP/XUUxguLc1WQosXL77idUREypPElHxGR8by5YoUCi657eflbsPEof4M7OyJre21/bkrIjdPqUu2x8bGYjAYrnpF+8Xq1Klj+jkuLu5GDOumsLe3B8DBwcGsvaT7HPn4+JTouIYNGwLw77//mrUfP36cqKgonJ2d6dSpk0VcSEgIYF5tADDty17YX5IYERG5OkEV7fl4dCD31nWy6Fv02znGfBzHuYyCIiJFREREREqXx1u70eMhN7O29CwjY2bGEZecV0yUSPkSGRlJdHS0RfvBgwfp06cPRqORfv36YWtrW2R8YQK8a9eul93X/I8//mDp0qXk55uvmDxx4gTdunUjPT2dLl26FFuGPiMjgyVLlgBXXkE/bdo0Tp8+bdZmNBr55JNPmDp1KgaDgZdeeumy5xARKQ+2R2Xy/Idn2XHQcgvJe+s68dnrFYu8RygipUOpqzXh5OREdnY26enp13yOi/cmL2ni+lZLSkoiPDwcwCLR3bZtW+zs7Fi4cCHDhg0zW93+3XffsXfvXlq3bk2tWrVKdK1jx44BEBgYaNa+e/duAO666y5T4v9ihU+W7tmzx9SWnJzMqVOnAGjUqJFFTNWqVfH19eXkyZOkpqZednIvIiKX51bBho8G+zF7STKLfztn1rc9KovBE6P5YJAf1QIt/wwXERERESlNXuzmSUxiHht2Zpra4pLzef3jOKaFBuDiXOrWg4jcUuHh4YwYMYKGDRsSHByM0Wjk5MmT/P333xQUFNCqVSvGjx9fbPw333wDXDkBfujQIZ555hkCAwNp3Lgxnp6eputkZWVRv359Pvvss2Ljly5dSlpaGvfddx933HHHZa81bdo0XnnlFRo3bkyNGjXIyspi7969HD9+HBsbG6ZPn86999572XOIiNzO8vONfPlTCgvWpGK8ZDW7jQ0885gHvTu6Y3PpnjwiUqqUumR7xYoVSU5OZt++fdecrN20aZPZ+UqDw4cPM27cOAoKCoiJieGPP/4gLS2NQYMG0bdvX7Njg4ODmTp1KsOHD6dhw4a0bNkSf39/Dh8+zM6dO+ncuTNz5swp8bULS7p37drVrL0waV7ck6qF7SdPnrSI8fLyKnZPpSpVqhAfH8/Jkydp0KBBkcdkZ2eTnX3hKa3U1NSSvBURkXLH1tbA4BAvala2Z9qCRHIvWvhzJjaPwROjefNZX+6v72y9QYqIiIiIXIGNjYHXB/iSmBrLvqMX7gccPZPLe1/EM+4lP+xUFlXKsXHjxvHzzz+zfft2Vq9eTWZmJt7e3nTo0IHevXvz9NNPY2NT9EMp27Zt49ChQ/j7+9OxY8fLXuf+++/npZdeYuvWrfz1118kJSXh4uLCPffcQ48ePXjppZdwdi7+35cXl5C/kpEjR7JmzRr279/PgQMHyM3NpWLFijz11FMMGzaM++6774rnEBG5XcUm5vHBlwlm86JCfp62vDnQhwbBWs0uUhaUumR7q1atiIqKIjs7mw8//NC033hJxcTE8Mknn5h+3bJlyxs9xGsSExPDvHnzzNqGDRvG+++/X+REeciQIfj5+TFw4EDWrVtnaq9YsSIdOnTA29u7RNedPXs2v/76K56enrz22mtmfWlpaQBUqFChyNjCZPq5c+dKHFNc3KXGjx/P2LFjS/AOREQE4D/NXanqb887n8WRlHqhfHx6lpHXP47j+cc9ebK92zXvlyciIiIicrM52Bt4/0Vfhk6O4UzshadI/zqQxbQFiYzs6635rJRbffv2tViQU1JNmzbFeOmSyGLceeedfPzxx9d0HYCff/65xMcOHTqUoUOHXvO1RERuV5v3ZDBpfiKp6ZZbRDa7y4nR/XzwcC162xARKX1KXY2uXr16mX6ePHkykyZNKnHsmTNnePjhh0lMTMRgMNCyZcvrXtnerVs36tate1Wvbdu2WZynRYsWGI1G8vLyOHbsGOHh4cydO5cmTZpw4sQJs2ONRiMjRoygV69e9OvXj0OHDpGWlsbWrVsJDg5m6NChDBky5Ipj37hxI8OHD8dgMDBnzhwqVap0XZ/FjTRmzBhSUlJMr0v3bxIREUt31XJk1quB1K5qXjbeaIRPlyQzfl4CObklu8EiIiIiImINHq62jB/sh6er+S2pn/9I5+tVqnonIiIit6+cXCMzfkjirdnxFol2O1t4OcSTcS/5KdEuUsaUupXtrVu3plOnTqxcuRKj0chrr73GDz/8wLBhw3jkkUfw9fU1O76goIBdu3axYMECZs+ebdqv3WAwMHHixOsez/Hjxzl48OBVxVy8Z/ylbG1tqVGjBmFhYVSvXp3u3bszdOhQVqxYYTpm3rx5RERE0LVrV2bNmmVqb9q0KStXrqRu3brMmjWLl19+mfr16xd5nX379tG1a1dycnKYPn063bp1szjG1dX1suNNT08HwM3NrcQxxcVdytHREUdHx2L7RUSkaP7edkSMDGDS/ETW/W3+Z/Gv2zI4E5vHey/44utZ6v6KFxEREREBoLKfPeNe8iNsWizZFz0s+uWKFAK8bOnYzNWKoxMRERG58f6Jy+X9LxI4dCrHoq+irx1vP+vDHUHKmYiURaVuZTvA3LlzufPOO02//vvvv+nfvz8BAQFUrFiRu+66i8aNG1O7dm1cXFy47777mDJlCunp6aaSSVOnTqVp06bXPZZdu3ZhNBqv6tWmTZsSnbtbt264urqyatUqcnIu/AE7f/58AEJCQixi3NzceOSRRzAajWZ701/s+PHjdOzYkaSkJN59991iyzVVq1YNOF8RoCiF7UFBQRYxSUlJpqR6SeJEROTGcXKw4c2BPjzbxYNLq2z+70QOL02IIeqE5X5PIiIiIiKlxZ01HHlzoI/FfHbS14nsOJhlnUGJiIiI3ATrtqfz4vjoIhPtbRpX4JMxgUq0i5RhpTLZ7uPjw7p163j00UdNCWw4X149JiaGqKgodu/ezbFjx8jOzjbbk8jDw4N58+aVif2ADAYD3t7e5OXlkZSUZGovTFZ7eHgUGVfYfnFMobNnz9KhQwfOnj3L8OHDeeedd4q9fsOGDYHzq+Bzc3Mt+nfs2AHA3XffbWrz9PQ0Jdx37txpEXP69Gni4+MJCgrC3d292GuLiMj1MRgM9H3Eg/de9MXZ0fwOZUJKPiOmxPDfrUU/FCUiIiIiUho82LACg0O8zNryC+CdT+M4/q/lzWgRERGRsiQ7p4Ap3yby/pwEMrLMt360t4PQ3l689awPrs6lMlUnIiVUan8H+/v789NPP7F06VI6duyI4aJHnS9eRV4oICCAMWPGcODAAZ5++mlrDPmqHTt2jNOnT+Pu7m5WHj8wMBCA7du3FxlX2F69enWz9qSkJB5++GGOHj3KM888w9SpUy97/Ro1anDnnXeSmZnJypUrLfoXLVoEQOfOnc3aO3XqZNZfkhgREbk5Hry7AjNGBVDR17xsfG4ejJ+XwCc/JpFfoH3cRURERKR0eqKtGz0eMt+GLj3TyJiZccQn51lpVCIiIiLX5+TZXF6eGMNPm9Is+qoF2PHxq4F0bulmlvsSkbLJYLw4Y12Kpaens337do4cOUJycjLZ2dl4eHjg5+dH48aNCQ4OtvYQixQZGUmPHj1MCfRCBw8epH///mzdupUhQ4YQGRlp6ps6dSphYWG4urqydu1as3L4M2bMYOjQobi5uXHixAm8vb2B83uot2/fnj///JOePXvy7bffYmtre8Xxff755zz//PPUrl2bTZs24e/vD8CPP/5I9+7dCQ4OJioqCju7C0mcgwcPUr9+fezs7Fi/fj3NmjUD4PDhwzRv3pyUlBSioqKu6jtJTU3Fw8ODlJQUrYgXEbkGKWn5vPdFPDsPWpaPb1rfiTcH+uopWZH/p3lH2aHvSkSkfCgoMPLeF/Fs2Jlp1h5cxZ5pYQFUcNI8Vm4uzTnKDn1XIlLaGY1GVm9JZ/rCJLJyLNNvDzdzYVhPL5w1vxEp9Uo67ygzyfayqnr16pw+fZqGDRsSHByM0Wjk5MmT/P333xQUFNCqVStWrlyJq6urKSYrK4sOHTqwadMmbGxsaN68OZUqVWL//v0cOHAAW1tbvvzyS7MV/KGhoUybNg1bW1uefPJJ7O3tixzP3LlzzX5dUFBASEgIS5YswcvLi4ceeoj4+Hh+//13nJycWLduHffff7/FeQofCLCzs6NDhw44ODiwZs0aMjMzmT59+lWX8ddEWUTk+uXlG5m1OIkl6y2fmK0aYMf4wf5UumQFvEh5pHlH2aHvSkSk/MjOKeCV6bHsP2ZePv6+ek6Me8kPO1ut+pKbR3OOskPflYiUZplZBUz7LpH/bsuw6HNyNDDiSS86NnMtIlJESiMl20uJb775hp9//pnt27cTHR1NZmYm3t7e3HPPPfTu3Zunn34aGxvLJ5hycnKYMWMGCxcuJCoqiszMTPz8/HjwwQcZOXKkaTV5oQEDBjBv3rwrjqeorzs/P5+IiAjmzJnD0aNHcXFxoW3btowdO5Z69eoVe64VK1YwadIk097tjRo14tVXX+Wxxx674jgupYmyiMiN89OmNKYvTCQv37zdy92GCYP9Ca7qYJ2BiZQSmneUHfquRETKl5S0fIZOjuFMrHn5+EcfdGFkH2+VWZWbRnOOskPflYiUVkdO5/DeF/EW8xiAmpXteftZX6oFFr1IUkRKJyXbpUzRRFlE5MbacySLdz+NJzmtwKy9gpOBDwb5cU8dJyuNTMT6NO8oO/RdiYiUP//E5jJkcgwpl8xjB3b24Kn/eFhpVHK705yj7NB3JSKljdFoZPnGND5elESuZZ6dLi1deam7J44OKhsvUtaUdN6h390iIiK3obuDnfh4dCA1K5s/MZuRZWT0jFg27LQsZyUiIiIiYm2V/e0Z95IfDvbmq9jnrEjhv1vTrTQqEREREUsZWQV8MCeBiO8sE+0uTgbefs6XEb29lWgXuc2V+o1bT5w4webNm/nf//5HcnIyGRkZRZZCL4rBYOCLL764ySMUEREpnQJ97JgWFsBbs+PYfTjb1J6bB2M/j2dELy86t3Sz4ghFRERERCzVq+HIG8/48O5n8Vx8C2jS1wn4etrS6A5VaRIRERHrOvZPDu9+VnTZ+DuCHHjrWV8q+Zb6FJyI3ACl9nf6X3/9xSuvvMKmTZuu6zxKtouISHnm6mzDhCH+jPsyno27Mk3tRiNMXZBE0rkCnv6Pu/a/FBEREZFSpeU9FRgc4sWMH5JMbXn58PancUwfGUCNSg5WHJ2IiIiUZ6u3pDFtQRLZuZYLQ3s85MZzXT2xt9O9NpHyolTWrvj222954IEH2LRpE0aj8ZpfIiIiAg7258tWPdbC1aJv7k8pTF+YRH6B/t4UERERkdLlibZudG9nXokpPdPImJlxxCcXsSmqiIiIyE2UnVPA5K8TmPBVokWi3dXZwPuDfHmpu5cS7SLlTKlb2X706FEGDhxIfn6+aZVdUFAQDz74IJUrV6ZChQpWHqGIiEjZY2tjILS3F97uNnz1c6pZ37INaSSdy+f1Ab4We2OKiIiIiFjToCc8iU3MM6vSFJuUz+uz4ogIDcDZqVSuIxEREZHbzJnYXMZ+Hs/RM7kWfXWqOfDOc75UVNl4kXKp1P3Onzp1Kjk5ORgMBtzd3ZkzZw7dunWz9rBERETKPIPBwIDHPPF0syXy+ySz/S837MzkXEYs773gh4uzbliKiIiISOlga2Pg9QE+jIyI5cDxHFP7kdO5jP0innGD/LC11QOjIiIicvP8viODSV8nkJFlWRmyaytXXurupQUsIuVYqbubvnbtWtPPCxYsUKJdRETkBnu8tRtvDfTBzta8fefBbEKnxZCYmm+dgYmUE3v27GHIkCE0a9aMSpUq4ejoiIeHB82bNycyMpLcXMun5At9//33tGvXDi8vL+zt7QkICKBr166sX7/e4tjk5GS+/fZbevfuTY0aNXBwcMDNzY3777+fiIiIy14nPz+fqVOn0qBBA5ydnfHz86Nnz55ERUVd9r2tWLGC1q1b4+7ujru7O23atGHlypUl/mxERESK4uhgwweD/KjsZ75mZNv+LKZ9l6itBEVEROSmyM0zMuP7RMZ+Hm+RaHdyNPDGMz4M7+WtRLtIOWcwlrJ/kbi5uZGRkUG1atU4fvy4tYcjt0hqaioeHh6kpKTg7u5u7eGIiJQLO/6XxVufxJGZbT4VqORnx8Sh/lRS6Su5TVl73jFjxgyGDh1KUFAQwcHB+Pn5ERcXx+bNm8nKyqJ169asWbMGBwcHs7jQ0FCmTZuGnZ0dLVu2xM/PjyNHjrBjxw4APvnkE1544QXT8W+++Sbjxo3DYDBwzz33UKdOHdN1srOzadGiBatXr7bYpqmgoICQkBCWLFmCp6cnDz30EPHx8WzYsAFnZ2fWrVtH06ZNLd7XtGnTCA0Nxc7Ojvbt2+Po6MiaNWvIzMwkMjKSIUOGXPVnZe3vSkRESpd/YnMZMjmGlLQCs/ZhT3rxeGu3YqJErkxzjrJD35WI3CoxiXm893k8USdyLPqqV7Tn3ed9qRZob4WRicitUtJ5R6lLtnt6enLu3DnatGljtspdbm+aKIuIWMehUzmMmRlL0jnzG5Ze7jZMGOxPcFWHYiJFyi5rzzuOHTsGQM2aNc3aY2JiaN++Pfv27bNITu/Zs4eGDRvi6enJ5s2bqVevnqnvu+++o0+fPlSoUIHo6GhcXV0BGD9+PMnJyQwePJhq1aqZjj98+DDt27fn1KlTjBkzhg8//NBsHJ9//jnPP/88tWvXZuPGjQQEBACwePFiQkJCCA4OJioqCju7Cw/kHDx4kPr162NnZ8e6deto3rw5AIcOHeKBBx4gJSWFqKgogoODr+qzsvZ3JSIipc/+Y9mMjIglJ/fC7Sw7W5g+MoC61R2tODIpyzTnKDv0XYnIrbB1fybj5yaQml5g0dfxfheG9/LC2bHUFY4WkRuspPOOUvenQVBQEEajkbS0NGsPRURE5LZXp5oD00cGUNHHvKZ8UmoBoVNj2HUoy0ojE7l91axZ0yLRDhAQEMDo0aMB+O2338z6NmzYAMCTTz5plmgH6NWrFw0aNCA9PZ0DBw6Y2seMGcOECRPMEu0AtWvX5qOPPgLOb9t0qSlTpgAwceJEU6IdoHv37nTp0oUjR46wbNkys5iIiAjy8/MZNGiQKdEOUKdOHd544w3y8vKIiIgo5hMREREpufo1HXl9gI9ZW14+vPdFPOcyLG+Ii4iIiJRUfr6RL5YlM2ZmnEWi3d4ORvb1ZnQ/byXaRcRMqfsToVOnTgDs27ePrCzd4BcREbnZKvvbM/2VQGpVMS99lZ5lZPSMWDbszLDSyETKH3v7878PLy0h7+hYspV6Pj4+Vz4IaNiwIQD//vuvWfvx48eJiorC2dnZNC+/WEhICHB+b/aLFe7LXthfkhgREZFr1apRBXp1MC8bH52Qz4SvErR/u4iIiFyTxJR8Rk2P5ZvVqRZ9lfzsmDkqkE4PumIwaH92ETFX6pLtL774IhUqVCArK4vZs2dbezgiIiLlgo+HLVNDA2hY2zyhl5sH730ez4qN56w0MpHyIykpifDwcACLRHfbtm2xs7Nj4cKFZqvX4XwZ+b1799K6dWtq1apVomsVlrIPDAw0a9+9ezcAd911lynxf7HGjRsD58vaF0pOTubUqVMANGrUyCKmatWq+Pr6cvLkSVJTLW9aiIiIXIuBXTxpUMt87vrHnkx+WKt5q4iIiFyd3YeyeGH8WXYdzrboa3mPM7NfC9RWiyJSrFKXbA8KCuLjjz/GaDTy+uuvs2bNGmsPSUREpFxwdbZhwhB/Wt7jbNZeYISpC5L46ucUrRQSuYEOHz7MgAED6NevHw8//DDVqlXjr7/+YtCgQfTt29fs2ODgYKZOnUpqaioNGzakXbt29OrVi3vvvZc+ffrQuXNnFi9eXOJrF5Z079q1q1l7YdK8SpUqRcYVtp88edIixsvLCxcXlxLHFSU7O5vU1FSzl4iISFHsbA28+awPHq7mt7Y+XZrMvqOWN8pFRERELlVQYOTbVSmMjIglMdW8bLytDbwc4sm7z/vi6lzqUmkiUoqUyj8h+vXrx1dffQXAo48+ygsvvMBff/1FQYH23hIREbmZHOwNvP2cL4+1cLXom/tTCtMXJpFfoIS7yI0QExPDvHnzmD9/PmvWrCEtLY1hw4YxYcIEbGwsp+lDhgzh22+/xcHBgXXr1rFw4UJ27NhBYGAgHTp0wNvbu0TXnT17Nr/++iuenp689tprZn1paWkAVKhQocjYwmT6uXPnShxTXFxRxo8fj4eHh+lVtWrVK7wbEREpz/w87Xh9gA8XV3MtKDi/f3vyuXzrDUxERERKvZS0fN6YFcfny1O49FaXn6ct08ICCGnnrrLxInJFdta4aM2aNUt0nK2tLQUFBXzxxRd88cUXODg44OPjY7GHZXEMBgNHjx69nqGKiIiUO7Y2BkJ7e+HlZsP8X8xXlS7bkEZyWgFj+vvgYK9/bEj51K1bN6Kioq4q5quvvqJp06ZmbS1atMBoNJKfn8+pU6dYsmQJY8eO5ZdffmHNmjVUr17ddKzRaCQ0NJSIiAgGDRpEWFgYlSpVYv/+/bzyyisMHTqUqKgoZs6cedlxbNy4keHDh2MwGJgzZw6VKlW6qvdxs40ZM4awsDDTr1NTU5VwFxGRy7qvnjNP/8edr36+MG+NT85n/LwExr/sh42N5qwiIiJiLupENmM/jyc20fLhvPvqOfH6AB88XG2tMDIRKYuskmw/ceJEiZ8GKjzOaDSSnZ3N2bNnSxRnNBr1xJGIiMg1MhgMPNPZEy93WyK/T+Li6vG/78ggNT2f917ww0VltKQcOn78OAcPHryqmIyMjGL7bG1tqVGjBmFhYVSvXp3u3bszdOhQVqxYYTpm3rx5RERE0LVrV2bNmmVqb9q0KStXrqRu3brMmjWLl19+mfr16xd5nX379tG1a1dycnKYPn063bp1szjG1dX1suNNT08HwM3NrcQxxcUVxdHREUdHx8seIyIicqmnH/Vg79Fsdh68UD7+rwNZfLs6laf+42HFkYmIiEhpYjQaWbI+jdk/JpF3SZ7dxgD9H/Og78PuelhPRK6K1e6QG43Gq3pdbZyIiIhcv8dbu/HWQB/sLnmYd+fBbMKmxZCYqvKcUv7s2rXrqueybdq0KdG5u3XrhqurK6tWrSInJ8fUPn/+fABCQkIsYtzc3HjkkUcwGo1s2rSpyPMeP36cjh07kpSUxLvvvsvQoUOLPK5atWoAnDlzpsj+wvagoCCLmKSkJFNSvSRxIiIiN4qtjYE3nvHF2938Ntfcn1LYeTDLSqMSERGR0iQ9s4D3vkhgxg+WiXYvNxsmDvPn6f94KNEuIlfNKivb161bZ43LioiIyDVoc68L7i62vPVJHJnZFx5oO3w6l2HhMUwc6k8lX6tMKURuOwaDAW9vb06dOkVSUhIBAQHAhWS1h0fRq/MK25OSkiz6zp49S4cOHTh79izDhw/nnXfeKfb6DRs2BM6vgs/NzcXe3t6sf8eOHQDcfffdpjZPT0+qVavGqVOn2LlzJy1atDCLOX36NPHx8QQFBeHu7n7Z9y8iInKtvN1teWugLyMjYk37rhYY4YMv4/lsTEW8PVQKVkREpLw6eiaHsZ/HcyY2z6KvQbAjbw30wddT97ZE5NpY5U+P1q1bW+OyIiIico0a13ViamgAr82IJTmtwNT+b1wewyZH89Fgf4KrOlhxhCK3h2PHjnH69Gnc3d3x9fU1tQcGBnLo0CG2b99O586dLeK2b98OYLbPO5xPvj/88MMcPXqUZ555hqlTp172+jVq1ODOO+8kKiqKlStX8vjjj5v1L1q0CMBiDJ06dWLWrFksWrTIItleXIyIiMiN1rCOEwM7e/D58hRTW1JqAR/MiWfScH9stVJNRESk3Fn1ZxrTvksiJ9eyInKvju4829kDW1vNEUTk2mmjVRERESmROtUcmP5KABV9zFcFJaYWEDo1hl2HVKJTpCQiIyOJjo62aD948CB9+vTBaDTSr18/bG0v/F4rTHpPmTKFbdu2mcXNmDGDjRs34ubmRseOHU3tGRkZdOrUib1799KzZ08+++wzDIYr30AICwsD4NVXXyU2NtbU/uOPP7J8+XKCg4Pp2rWrWczw4cOxtbVl9uzZbNmyxdR++PBhxo0bh52dHcOHD7/itUVERK5Xr47uNK3vZNa263A281amFBMhIiIit6OcXCNTvk1k4vxEi0S7q7OBDwb58sLjnkq0i8h1U7JdRERESqyKvz3TXwmkVhXz0tLpWUZGz4jl7/8p4S5yJeHh4VSuXJnGjRvTs2dPevToQdOmTalXrx5bt26lVatWjB8/3izmpZdeokWLFqSlpdG8eXNatGhBz549qV+/PkOHDsXW1paZM2fi7e1tinnjjTf4888/sbW1xc7OjmeffZYBAwZYvC41cOBAunXrxuHDh6lbty49evSgbdu2hISE4OzszNdff42dnXmBrDvuuINJkyaRnZ1Ny5YtefTRR3n88cdp2LAhCQkJTJkyheDg4JvyeYqIiFzMxsbAmP4++HuZPyD69S+pbNufaaVRiVzZnj17GDJkCM2aNaNSpUo4Ojri4eFB8+bNiYyMJDc31yLm3XffxWAwXPG1YcOGIq8ZFxfHK6+8wh133IGzszPe3t40btyYUaNGFTvOFStW0Lp1a9zd3XF3d6dNmzasXLnysu9t//799OjRAz8/P5ydnWnQoAHTpk2joKDgsnEiItcqNjGP4VNi+GlTmkXfHdUc+GRMRR64u4IVRiYityOD0Wi0rJ0hcoulpqbi4eFBSkqK9vIUESkD0jILeGt2HLsPZ5u1O9gb+PBlPxrf4VRMpIj1WXve8c033/Dzzz+zfft2oqOjyczMxNvbm3vuuYfevXvz9NNPY2Nj+UxsTk4OM2bMYOHChURFRZGZmYmfnx8PPvggI0eOpFmzZmbHDxgwgHnz5l1xPEX9cyA/P5+IiAjmzJnD0aNHcXFxoW3btowdO5Z69eoVe64VK1YwadIkdu7cCUCjRo149dVXeeyxx644jqJY+7sSEZGya/+xbEZMiSH/olyeu4sNn44JxN9be7KKudIw55gxYwZDhw4lKCiI4OBg/Pz8iIuLY/PmzWRlZdG6dWvWrFmDg8OF7buWLl3K0qVLizzfP//8w6+//kqFChWIiYnB1dXVrP/vv//m4YcfJiEhgfr163PXXXeRmprKgQMHOHPmDHl5lvsaT5s2jdDQUOzs7Gjfvj2Ojo6sWbOGzMxMIiMjGTJkiEXMn3/+yUMPPURmZiZNmzalevXqbNiwgejoaHr06MHChQtLVH2pUGn4rkSkdNvxvyzenxNPSprlAz1dW7nyUncvHOy1ml1Erqyk8w4l26VU0ERZRKTsyck18sGceDbtNl8h5GhvYPxgP+6po4S7lE6ad5Qd+q5EROR6/LA2lVmLk83a6td0YGpoAHYqGSsXKQ1zjmPHjgFQs2ZNs/aYmBjat2/Pvn37ik1oF2X06NFMnDiRvn378vXXX5v1xcXFUa9ePTIyMliwYAFdunQx69+2bRtNmzY1azt48CD169fHzs6OdevW0bx5cwAOHTrEAw88QEpKClFRUWbVjHJzc7njjjs4fvw4U6ZMITQ0FIC0tDQ6duzIn3/+yZdffllktaXilIbvSkRKJ6PRyII1qcxZnkLBJVkvR3sDYX286XC/i3UGJyJlUknnHVZ5lPerr74y+3W/fv2K7bteF59bREREbhwHewPvPOfLh/MSWLc9w9SenWvk9Y/jGD/Yj4a1lXAXEREREesIaefGniPZbL7o4dD9x3L4fFkyg57wsuLIRCxdmmQvFBAQwOjRo3n66af57bffSpRsNxqNLFiwAICnn37aov+dd94hPj6emTNnWiTaAYtEO0BERAT5+fkMGTLElGgHqFOnDm+88QZhYWFEREQQGRlp6luyZAnHjx+nYcOGpkQ7gKurKzNmzODee+8lPDz8qpLtIiJFSc8sYMJXCRYLQgAq+dkx9nlfalVxKCJSROT6WSXZPmDAAFN5IIPBYJYQv7jvel16bhEREbmxbG0NvN7fh4IC+H3HhYR7Vo6RMR/HMWGwHw2ClXAXERERkVvPYDAw+mkfXjxzlrMJ+ab27389R4NajjzYUHu1Stlgb28PYFZC/nLWr1/P6dOnCQwMpH379mZ9mZmZfP3117i4uPDMM8+UeAyF+7KHhIRY9IWEhBAWFsaKFSvMku2Xi2ncuDE1a9Zk3759nDhxgurVq5d4LCIiFzv+bw7vfBrPmVjL7S+aN3BmTH8fXCtYbtUmcjNt27aNWbNmsX79es6ePYufnx9t2rTh9ddf584777T28OQGs+qfMEajscg9Igvbb8RLREREbi5bWwNvPONDq0bOZu1Z2UZemxnHvqPZxUSKiIiIiNxcrhVseOd5P+wvWW4y4asEzsZb3pQXKW2SkpIIDw8HoFOnTiWKKSwb37t3b2xtbc36tm/fzrlz52jUqBHOzs788ssvhIWF8fLLLzNt2jT+/fdfi/MlJydz6tQpABo1amTRX7VqVXx9fTl58iSpqamm9t27dwPnE+tFKWzfs2dPid6XiMil1m1PZ/DEGItEu8EAAzt78P6Lvkq0yy2Vn59PaGgozZo1Y+7cuZw4cYLs7GzOnDnD119/zf3338/mzZutPczLio2N5aeffuLtt9/mP//5D76+vhgMBgwGg6rRFMMqK9tbtWpV7Or1y/WJiIhI6WRna+DNgb6M/TzerExnZraR12bGMnGoP/VqOFpxhCIiIiJSXtWp5sDLIV5EfJdkakvLNPLe5/FEjAzAwV73oaT0OHz4MOPGjaOgoICYmBj++OMP0tLSGDRoEH379r1ifFZWFosXLwaKLiF/4MABAPz9/Xn88cdZtmyZWf/rr7/OF198Qe/evU1thYl2Ly8vXFyK3u+4SpUqxMfHc/LkSRo0aGAWV6VKlWJjAE6ePFns+8nOziY7+8ID3Bcn80Wk/MrLN/LpkmQW/XbOos/dxYY3nvHhvnrORUSK3DwFBQX079+fb775Bjj/93Dfvn1xd3dnxYoVTJgwgXPnzjFgwAD27duHo2PpvFcaEBBg7SGUOVZJtq9fv/6a+kRERKT0srM18Paz5xPuf+y5kHDPyDIyOjKWicP8ubN66ZxEioiIiMjtrUtLV/YcyWbd9gtbHx08lcPsH5MY9qS3FUcmYi4mJoZ58+aZtQ0bNoz3338fG5srr85cvnw5KSkp1K9fv8hV6ElJSabjbG1tmTlzJj169CAjI4MZM2YwefJk+vfvz5133sk999wDQFpaGgAVKhS/9UJhEv7cuQuJryvFFRVzqfHjxzN27Nhi+0Wk/ElMyWfsF/HsPWJZSbFONQfefd6XQB+rpL6knJswYQLffPMNBoOBL7/8kv79+5v6mjdvjo2NDePGjePIkSMsX76cHj16WHG0JVOtWjXq1q3LmjVrrD2UUk31M0REROSGsbc7n3Bvdpf5Pu3pWUZejYzl4EmVlBcRERGRW89gMDCyjzdV/M1vvi/9PY31f6dbaVRyO+nWrRt169a9qte2bdssztOiRQuMRiN5eXkcO3aM8PBw5s6dS5MmTThx4sQVx1FYQr6oVe1wftUdQF5eHu+99x4vv/wyfn5+BAUFMWnSJHr06EFubi6TJk269g/jBhozZgwpKSmm1+nTp609JBGxon1Hs3nxo+giE+3/ae7C9JEBSrSLVezbt4933nkHgBEjRpgl2guFhYWZHpxbuXLlLR3f1Xj77bdZsWIF0dHRnDx5kk8++cTaQyr19KeOiIiI3FAO9gbefd6Ptz+NY9v+LFN7eqaRUdNjmTw8gDrVHKw4QhEREREpjyo42fDu8768PDGGnFyjqX3yN4nUquJA1QB7K45Oyrrjx49z8ODBq4rJyMgots/W1pYaNWoQFhZG9erV6d69O0OHDmXFihXFxiQkJLBq1SpsbGyKLTnv6upq+vmZZ56x6H/mmWf44Ycf+P333y1iLjfe9PTzD624ubmZxSUlJRUbV1TMpRwdHUttmV0RuXWMRiNL1qcxa3ES+QXmffZ2MOxJbzo96Fp0sMgt8Pbbb5Obm4unpyfvvvtukcd4e3tTvXp1jh07xqFDh27tAK+CKspcPa1sFxERkRvOwd7Aey/4cV898xXuaf+fcD9yOsdKIxMRERGR8qxmZQeG9/Iya8vIMjL283iycwqKiRK5sl27dmE0Gq/q1aZNmxKdu1u3bri6urJq1Spycor/t9TChQvJzc2ldevWxe6THhQUBJwv7e7n52fRX716dQBiY2NNbdWqVQPOl6AvTJBf6syZM2bnvziusK8kMSIil8rMLuDDuQnM+MEy0e7vbUtEWIAS7WJVBw8eZOnSpQA8++yzuLu7F3usp6cncH7bGLl9KNkuIiIiN8X5hLsv99Y1T7ifyyjglemxHD2jhLuIiIiI3Hr/ae7Kw81czNqO/ZNL5A9JVhqRyOUZDAa8vb3Jy8sz7blelCuVkAdM+7hnZmaSnW1ZhjkxMREwXwHv6elpSpzv3LnTIub06dPEx8cTFBRklmBo2LAhADt27ChyLIXtd999d7HjFZHy7UxsLkMmxbD2L8sKGffWdeKT1wKpW13VL8S65s+fj9F4vmpSv379Lntsfn4+AHZ2Kjx+O1GyXURERG4aRwcb3h/kS6M7zP/hk5p+PuF+/F8l3EVERETk1hvey4salczLxv+8OZ01W9KsNCKR4h07dozTp0/j7u6Or69vscf8+eefODs7071792LPVa1aNRo2bIjRaDQrFV+osK0wKV+oU6dOACxatMgiprCtc+fOJY7ZuXMnx44d46677jKtphcRudjm3Rm89FE0x//Ntejr+7A7Hw3xw8PV1gojEzG3cOFCAGrVqnXFB8jS0s7PNV1cXC57nJQtVnl0ol27drfkOgaDgbVr196Sa4mIiEjRnBxsGPeSH6/PjGPX4QsrJ1LSChg5LZYpoQFUr6j9MUVERETk1nFysOGd53wZNCGarOwL+7dP+y6J2tUcqFHJwYqjk/IoMjKSHj16EBgYaNZ+8OBB+vfvj9FopF+/ftjaFp1YKlzV3rVr18uWrwV49dVX6du3L6+88gqrV6+mYsWKwPlS+OHh4QAMGjTILGb48OF8+umnzJ49m169etGsWTMADh8+zLhx47Czs2P48OFmMd26daNGjRrs3r2bqVOnEhoaCpzfq33w4MEAjBw58oqfjYiUL/kFRuauSOGb1akWfS5OBl7r78ODDStYYWQilk6fPs2RI0eA8w+q7du377LHJyQkAJT4QTODwXBd4wP48ssvGTBgwHWfR4pnlWT7+vXrb8j/IJdjNBpv+jVERESkZJwcbBj3sh9jZsax58iFhHtyWgEjI2KYOiKAaoFKuIuIiIjIrVMt0J6RfbwZ92WCqS0rx8h7n8fz8auBODupIKTcOuHh4YwYMYKGDRsSHByM0Wjk5MmT/P333xQUFNCqVSvGjx9fbPw333wDXL6EfKE+ffqwZs0a5s2bR7169XjggQfIzMzkjz/+IDs7m+eff54ePXqYxdxxxx1MmjSJsLAwWrZsSYcOHXBwcGDNmjVkZmYyffp0goODzWLs7e35+uuvad++PWFhYSxcuJCgoCA2btzI2bNnCQkJoX///tfwaYnI7SolLZ8P5iTw9/+yLPqqV7Rn7Au+VA3Q/SMpPTZu3Gj6edGiRUVWcynKpX9nStlmtU0BCvcvEBERkfLB2dGG8S/78drMOPYevZBwT0otIGxaDFNCA6imfzCJiIiIyC300H0u7DmSzYqNF8rHn4zOY+qCRMYM8NFCDrllxo0bx88//8z27dtZvXo1mZmZeHt706FDB3r37s3TTz+NjU3RD4Bs27aNQ4cO4e/vT8eOHUt0vS+//JIHH3yQTz75xLQwqnHjxrz44ovFJsBDQ0MJDg5m0qRJpuRCkyZNePXVV3nssceKjHnggQf466+/eOedd1i/fj27d++mVq1ajBo1iuHDh+v3mIiYHDyZzTufxRObmG/R165JBUb29cbZUQ/CSemya9eua4q7dLuW4uzdu/eazn+xKlWqXPc55PIMRitkvefNm3fLrmXtpyP37NnDp59+yvbt2zl16hQJCQk4OTlRr149+vTpw6BBg7C3Lzqx8P333zN79mx27txJWloa3t7eNGvWjNDQUNq0aXPFax8+fJi7776brKwsHnroIX799dcij8vPz2f69OnMmTOHI0eO4OrqStu2bRk7dix33nlnsedfsWIFkydPZufOnQA0btyYUaNGmfZjuhqpqal4eHiQkpJyxVJXIiJStmVkFTB6Riz7j5nv1+7jYcvUUH+q+CvhLjeX5h1lh74rERG5FXJyjQyZHM2R0+Z7wob18eaxFq5WGpXcSppzlB36rkRuTys3pzF9YSK5eebttjbwUncvurVx1cM5Uip169aNpUuX4uLiYtqPvThvv/0277//PgBnzpyhcuXKt2KI1+3EiRPUqFEDOJ9znTt3rnUHdAuVdN5hlZXt1k6A30obNmxg5syZBAUFUa9ePfz8/IiLi2Pz5s1s2bKFxYsXs2bNGhwczPcCCw0NZdq0adjZ2dGyZUv8/Pw4cuQIy5cvZ/ny5XzyySe88MILl732Cy+8QHZ29mWPKSgooEePHixZsgRPT086depEfHw8ixYtYuXKlaxbt46mTZtaxE2bNo3Q0FDs7Oxo3749jo6OrFmzhscee4zIyEiGDBly9R+WiIiUCxWcbPhosD+jZ8Ry4PiFhHtCSj5h02KZOsKfykq4i4iIiMgt4mBvOL9/+/ho0rMurEmJ/D6RukEOBFfV/u0iIiI3Q06ukekLE/n5j3SLPm93G955zpcGwU5WGJlIyZw8eRKAatWqXfHY//73vwDUqVOnzCTapWSsVka+vHj00Ud59NFHqVmzpll7TEwM7du35/fff+fTTz81S07v2bOHadOm4enpyebNm6lXr56p77vvvqNPnz6EhYXRp08fXF2LfsL6iy++YP369bzwwgt8+umnxY5vzpw5LFmyhNq1a7Nx40YCAgIAWLx4MSEhIfTt25eoqCjs7C78r3Lw4EFeeeUVHB0dWbduHc2bNwfg0KFDPPDAA4SGhvLII49ozwkRESmWi7MNHw3x59XIWP534kLCPT75/xPuYQFU8tU0RURERERujcp+9ox62od3P4s3teXmwbufxzP7tUBcnVW2VkRE5EaKS8rj7U/jOXgyx6KvQS1H3n7OFx8PWyuMTKTkMjIyAPDx8bnscf/88w9bt24F4Mknnyzx+fft23ftg/t/VapUwdPT87rPI8XTvxRuspo1a1ok2gECAgIYPXo0AL/99ptZ34YNG4Dzv+EuTrQD9OrViwYNGpCens6BAweKvGZMTAyjRo0y7el0OVOmTAFg4sSJpkQ7QPfu3enSpQtHjhxh2bJlZjERERHk5+czaNAgU6Idzj+N88Ybb5CXl0dERMRlrysiIuLqbMPEIf7cUc18pVBccj5h02KITsgrJlJERERE5MZr1agC3du5mbX9G5dH+DeJWGEXRhERkdvW3iNZDPooushEe/d2boSP8FeiXcqEvLzz9y8dHR0ve9zXX3+N0WjEYDDw1FNPlfj8DRo0uO7X0qVLr+ctSgko2W5FhXu1X1pC/kq/KQsV96TM8OHDyczM5OOPP75s/PHjx4mKisLZ2bnIfdZDQkKA83uzX2zlypVm/SWJERERKYprBRsmDvMnuKp52fjYxHxCpyrhLiIiIiK31guPe3JndfP7NL/vyOD3HRlWGpGIiMjtw2g0smzDOcKmxZJ0rsCsz8nBwJsDfRgc4oWdrfZnl7KhcBFrampqscdkZWUxffp04HwOrU6dOrdkbHLrlJlk+8GDB1mwYAGRkZF88MEHvPfee9Ye0nVJSkoiPDwcwCLR3bZtW+zs7Fi4cKHF6vXvvvuOvXv30rp1a2rVqmVx3p9//pmFCxfy+uuvX7GM++7duwG46667TIn/izVu3Bg4X9a+UHJyMqdOnQKgUaNGFjFVq1bF19eXkydPXvYPFxERkUJuFWyYNNSfWlXM/y6KScxn5LQYYhKVcBcRERGRW8PezsDbz/ni7mJ+y2z6wiSSz+VbaVQiIiJlX06ukfBvEon4Lol88zw7FX3tmDEqgHZNXKwzOJFrVLt2bQD2799PTo5lpQaADz74gH///RcHBwfGjh17Vec3Go3X/RowYMD1vk25glKdbD937hzjxo2jatWq1KtXj6eeeooRI0bwzjvvFPs/ZK9evXjggQd48MEH+eeff27xiIt3+PBhBgwYQL9+/Xj44YepVq0af/31F4MGDaJv375mxwYHBzN16lRSU1Np2LAh7dq1o1evXtx777306dOHzp07s3jxYotrpKen8/LLL3PHHXeYStRfTmHSvEqVKkX2F7afPHnSIsbLywsXl6L/4isq7lLZ2dmkpqaavUREpPzycLVl8jB/alYyT7ifTTi/h3tckhLuIiIiInJrBHjbMaK3t1lbcloBM35IstKIREREyra45DxCp8bw8x/pFn1N7nRi1ugAalZ2KCJSpHR79NFHgfN7t3/yyScW/QsWLGD8+PEAjBs3jjvvvPOWjk9uDTtrD6A4W7dupXfv3qaE7aV7YxkMRZcReeCBB/j+++8xGAx89dVXjBkz5qaPtSRiYmKYN2+eWduwYcN4//33sbGxfOZhyJAh+Pn5MXDgQNatW2dqr1ixIh06dMDb29si5s033+TkyZOsW7fOojR9UdLS0gCoUKFCkf2FyfRz586VOKa4uEuNHz/+qp/gERGR25uHqy2Th/szMiKW4//mmtrPxucRNi2WKaH++HmW2qmLiIiIiNxGWjdypkVDZzbtzjS1/bY9g7b3ZvBgw+LviYiIiIi5fUezefezOBJTCyz6enV059kuHtjaqGy8lE1dunShZs2aHDt2jJEjR5KcnEzHjh1JTU3lm2++MeUF+/fvz8iRI6082pLZtGkTR44cMf06Pj7e9PORI0eYO3eu2fFaOV9Kk+07duygY8eOpKWlmZLswcHB3HXXXWzbto1///232Nj+/fszatQo8vLyWLx48XUn27t160ZUVNRVxXz11Vc0bdrUrK1FixYYjUby8/M5deoUS5YsYezYsfzyyy+sWbOG6tWrm441Go2EhoYSERHBoEGDCAsLo1KlSuzfv59XXnmFoUOHEhUVxcyZM00x27dvZ/r06fTr1482bdpcz1u+JcaMGUNYWJjp16mpqVStWtWKIxIRkdLA0+18wj1sWiwnz15IuP8Tl8fIabFMDQ3Ax8PWiiMUERERkfLAYDAwvJc3uw+f5VzGheTAtO+SaFjbCdcKpbpYpIiISKmwYuM5Ir9PIu+SnVicHAy8+rQ3be5V2Xgp25ycnPjyyy/p1KkTaWlpvP3227z99tumfhsbG8LCwpgwYUKxi4hLm88//9xi8XChzZs3s3nzZrM2JdtLYRn5vLw8evfubVoVfc8997BlyxYOHTrEjz/+SIMGDS4b7+HhQdu2bTEajezatYvExMTrGs/x48c5ePDgVb0yMjKKPZ+trS01atQgLCyML7/8ksOHDzN06FCzY+bNm0dERARdu3Zl1qxZ1K5dGxcXF5o2bcrKlSupVKkSs2bNYv/+/abP7Pnnn8fT05PJkyeX+L25uroCFDve9PTzJV3c3NxKHFNc3KUcHR1xd3c3e4mIiAB4udkSPtyfagHmzwSeic3j1chY0jItn4QWEREREbnRfDxsGRziadaWkJLPrMUqJy8iInI5OblGpnybyNQFlon2ij62RL4SoES73DZatWrFn3/+Se/evalcuTKOjo5Ur16dAQMGsHXrViZNmlRkhWu5fZS6le3z58/n8OHDGAwGGjVqxIYNGy5bsrwozZs3Z82aNRiNRvbs2XNdK7137dp1zbFX0q1bN1xdXVm1ahU5OTmm0u/z588HICQkxCLGzc2NRx55hDlz5rBp0ybq16/PmTNn2LVrF4GBgfTo0cPs+OTkZAD+/vtv0+ewfv16AKpVqwbAmTNnihxfYXtQUJCprTAmKSmJ9PT0IvdtLypORETkani72xI+IoDQqTGcib2wX/vxf3N559M4Phrsj71d2XgaVERERETKrg73u/Db3xls259lavvlz3Ta3FuB++o5W3FkIiIipVNCSj7vfBrHgeM5Fn331nXizYE+eLiqaqHcXu666y6+/fZbaw/jhpg7d65FqXi5vFL3KMXSpUtNP8+ePfuqE+1w/n/qQhfvK1DaGAwGvL29ycvLIynpwlPRhclqDw+PIuMK2y+OAYiOjub33383e+3evRs4n3QvbCvUsGFDAPbt20dubi6X2rFjBwB33323qc3T09OUcN+5c6dFzOnTp4mPjycoKEir1UVE5Lr4eNgyZYQ/VfzNnw3ceTCbSfMTTFvNiIiIiIjcLAaDgbDe3lRwMn/QM/zbRDKyVHFJRETkYgeOZzPoo+giE+1Ptnfjo8F+SrSLyG2n1CXbC1eSBwUF0aRJk2s6h7e3t+nnwpXdpdGxY8c4ffo07u7u+Pr6mtoDAwOB8/uwF6WwvXCf9+rVq2M0Got8rVu3DoCHHnrI1FaoRo0a3HnnnWRmZrJy5UqL6yxatAiAzp07m7V36tTJrL8kMSIiItfC19OOCUP88XI3n7L8+lcGXyxPsdKoRERERKQ88fe248VunmZtsYn5fLY02SrjERERKY1Wbk4jdGoMCSnmdeMd7Q28OdCHF5/wwtZWVQpF5PZT6pLtcXFxGAwGatSocc3nsLO7sAIuLy/vMkfefJGRkURHR1u0Hzx4kD59+mA0GunXrx+2thee5nr88ccBmDJlCtu2bTOLmzFjBhs3bsTNzY2OHTte9/jCwsIAePXVV4mNjTW1//jjjyxfvpzg4GC6du1qFjN8+HBsbW2ZPXs2W7ZsMbUfPnyYcePGYWdnx/Dhw697bCIiIgAVfe0Y/7I/To7m/yD7dnUqyzacs9KoRERERKQ86fSgK/fUcTRrW7Yhjd2Hs4qJEBERKR9y84xMXZBI+DeJ5F6Sjgn8//3Z2zXR/uwicvsqdcl2JycnALKzs6/5HPHx8aafL17lbg3h4eFUrlyZxo0b07NnT3r06EHTpk2pV68eW7dupVWrVowfP94s5qWXXqJFixakpaXRvHlzWrRoQc+ePalfvz5Dhw7F1taWmTNn3pD3NnDgQLp168bhw4epW7cuPXr0oG3btoSEhODs7MzXX39t9vACwB133MGkSZPIzs6mZcuWPProozz++OM0bNiQhIQEpkyZQnBw8HWPTUREpFCdag6885wvNpfMXCIXJrF5T4Z1BiUiIiIi5YaNjYGRfb1xcjB/AHTy14lk5aicvIiIlE+JKfmMjIhlxcY0i75Gdzgya3QgwVUdrDAyEZFbp9Ql2wMCAjAajRw+fPiaz7F161bTz1WrVr0Rw7pm48aNo1evXqSnp7N69WqWLVvGqVOn6NChA3PnzmXdunW4urqaxTg5ObF27VrCw8Np0qQJe/bsYcmSJSQlJRESEsKmTZt4+umnb8j4bGxs+OGHHwgPD6dSpUr89NNP7N27l+7du7N9+3buv//+IuNCQ0NZvnw5zZs3Z+PGjaxdu5YmTZqwYsUKhg4dekPGJiIicrH76zsT2tv8QbMCI3zwRQJRx6/9IT0RERERkZKo7GfPs108zNr+icvjyxXa3khERMqfqBPZDJoQzb6jlvdkejzkxsQh/tqfXUTKBYPx4k28S4GBAwcyd+5cDAYDv//+Oy1atDDr/89//sPq1asxGAzk5+dbxOfl5VGrVi1Onz6NnZ0dCQkJuLm53arhyzVKTU3Fw8ODlJQU3N3drT0cEREpxeb+lMxXP6eatXm42jDjlQAq+9tbaVRSlmjeUXbouxIRkdImv8DI8PAYDhzPMbXZGGD6KwHUq+F4mUgpzTTnKDv0XYmUDr/8mca0BZZl4x3sDYx6ypuH7lPZeBEp+0o67yh1K9sv3h/81VdfLTKhfjkffvghp0+fxmAw8NBDDynRLiIicpvp38mDh5uZ/6MtJa2A0TPjSD53dfMGEREREZGrYWtj4NWnfbC/aMe9AiNMmp9ATm6pWs8iIiJyw+XlG4lYmMik+ZaJdn9vW6aPDFCiXUTKnVKZbL/nnnuA8+XgQ0JCSE1NvXwQYDQa+fDDD3nvvfdMbW+99dbNGqaIiIhYicFwfr/MJnc6mbX/G5fHG7PitGemiIiIiNxU1QLt6d/JvJz8yeg8vl6lcvIiInL7SjqXzysRsSz73XJ/9nvqODJ7dCB1qml/dhEpf0pdsh3gs88+o0KFCgAsX76c4OBgXn31VX755RfOnTtnOm7nzp2sWrWKd999lzp16vDWW29RUFCAwWBg8ODBPPDAA9Z6CyIiInIT2dkaePd5X4KrmJeNjzqRwwdzEsgv0KoiEREREbl5nmzvbpFQ+HZ1KodP5xQTISIiUnYdPJnNoPHR7DliuT9793ZuTBrqj6eb9mcXkfKp1O3ZXmjVqlX07NmTtLQ0DAaDWV/hkItr7969OwsXLsTGplQ+SyBF0H5LIiJyLRJS8hk8KZrYRPPy8V1aujK8l5fFXEEENO8oS/RdiYhIaXb0TA6DPoom/6LCSsFV7Pl4dCB2tpqHliWac5Qd+q5Ebr3VW9KY8m3R+7OH9fGm4/0qGy8it6cyu2d7oUceeYQdO3bQsmVLjEaj6QXnk+wGg8Gs3Wg04urqykcffcQPP/ygRLuIiEg54ONhy4TB/rg6m9/MXL4xje/+e66YKBERERGR61erigN9HzG/6XbkTC7frbnydogiIiKlXX6+kRnfJzLhqyL2Z/c6vz+7Eu0iIqU42Q4QHBzM77//zpYtWxg8eDB33303NjY2Zon3ChUq0K5dOyZMmMDJkyd59dVXrTxqERERuZWCKtrz/iA/7O3M2z9bmsyv29KtMygRERERKRf6PuJBjUrmWxvN/yWFE2dzrTQiERGR65eans/oGbH8uN5yf/aGtR2Z9Zr2ZxcRKWR35UOsr2nTpjRt2tT065SUFNLT0/Hw8MDFRU9OiYiIlHcNazsxpr8P732RYNY+cX4C3h62NL7DyUojExEREZHbmb2dgVFPeTNkUgwF/79RY24eTJqfwPRXArC1UTl5EREpW06czeXN2XH8G5dn0fdEG1cGdffSdikiIhcpdSvb//777yse4+HhQaVKla6YaH/xxRdv1LBERESklGtzrwsvdfc0a8vLh3c+iePYPznWGZRIEfbs2cOQIUNo1qwZlSpVwtHREQ8PD5o3b05kZCS5ucWvhPv+++9p164dXl5e2NvbExAQQNeuXVm/fn2Jrn348GGcnZ0xGAy0b9++2OPy8/OZOnUqDRo0wNnZGT8/P3r27ElUVNRlz79ixQpat26Nu7s77u7utGnThpUrV5ZobCIiImVV3eqO9HjIzawt6kQOP67TtkYiIlK2bNmbyZBJ0RaJdns7GN3PmyE9vZVoFxG5RKlLtv/nP//h0KFD132eZ599ls8///wGjEhERETKipB2bjzR1vxGZ3qWkddmxhGXZPlEtog1bNiwgZkzZxIdHU29evV44oknuO+++9i1axfDhg2jQ4cO5ORYPiASGhrKk08+ycaNG2nUqBFPPPEEVapUYfny5bRt25ZPP/30itd+4YUXyM7OvuwxBQUF9OjRg7CwMM6cOUOnTp2oX7/+/7F332FRXd0awN+ZofcqSiyI2LCgRIlY0YgasAB2jRrNZ0IiNmzp0STGWLCXaGLELrFrrBBFFHvFigVRNNLLUIY65/7hZfQ4A4JtQN/f88xzZe1ZZ9bMfDcczjp7b2zZsgUtWrTA6dOnNebNnz8fPXv2xPHjx9GmTRt06tQJp0+fRvfu3bF48eKyfThERESV1CfdzVG9ingByZW7MvAgkcvJExFRxScIAjYdlOPb35OQkyuIxqzNZZg/3g5dW5loqToiooqtwjXbk5OT4enpiQcPHrzwMYYPH45Vq1a9wqqIiIioMpBIJPiitwXaNzcUxZPTi/D1kiRkKZRaqozoCS8vL9y5cwexsbEICwvDxo0bERYWhtjYWDRu3BhHjhxRa5xHRUVh/vz5sLCwwKVLl3Do0CGEhITg3Llz2LhxIyQSCQIDA5GVpb6fXrGVK1ciPDwcI0eOLLW+v/76C9u3b0fdunVx48YNbNmyBeHh4di8eTNycnIwePBgFBaKb16Jjo7GxIkToa+vj4iICOzbtw87duzAxYsXYW1tjfHjx+P27dsv/qERERFVcPp6Ukz62AqSpyb75RcImLMuFUqlUHIiERGRluXlKzEjOAUrdqRDeOZXVv2aelg6xQ4Na+trpzgiokqgwjXbAeDBgwfw9PREUlJSufIEQcCwYcOwZs2a11QZERERVXQyqQRfD7NG4zriPwRj/ivAjyuSUFDIi52kXY6OjnB0dFSL29nZYcqUKQCAQ4cOicYiIiIAAP3794ezs7NobMCAAWjSpAmys7Nx7do1ja+ZkJCASZMmwdPTEwMHDiy1vrlz5wIAZs2aBTs7O1W8d+/e6NmzJ27fvo2dO3eKchYsWICioiL4+/vD3d1dFa9Xrx6+/fZbFBYWYsGCBaW+LhERUWXXxMkAPh3Es/6ibudh97GSb4YjIiLSpuT0Qoybl4iwMzlqYx+2NML8wCqwtdDRkElERMUqXLP9gw8+gCAIuHnzJj766CNkZpZtfytBEDBkyBCsW7cOwv/ffjVjxozXWSoRERFVUPp6Uvz8uQ1q2In/ILwQnYfZa1NU5wpEFY2uri4AQE9PTxTX1y/bLAJra2uN8bFjx0KhUGDp0qWl5t+9exfXr1+HoaEhvL291cb79OkD4PHe7E8r3pe9eLwsOURERG+j//W0QFVrmSi2Yns64lO4pREREVUs12Pz8MXMBETfE29jJpEAI30s8M0n1tDXq3AtJCKiCqfC/Zdy7969cHZ2hiAIuHDhAnr06FGmfSUHDx6MjRs3qi6ez549G5MnT34TJRMREVEFZG4iw2+jqsDSTHy6E3YmByt3ZWipKqKSpaWlISgoCADUGt0dO3aEjo4OQkJC1Gavb9q0CZcvX0aHDh1Qp04dtePu3bsXISEh+Oabb+Dk5FRqDZcuXQIANG7cWNX4f5qrqyuAx8vaF0tPT8f9+/cBAM2bN1fLqVGjBmxsbHDv3j3I5fJSX5+IiKiyMzSQYsJg8c1vijwBczek8oZPIiKqMMJOZ2Pc3ASkZBSJ4kYGEvzib4uBXcwgeXpvFCIiKlGFa7ZbWlri4MGDqFWrFgDg6NGj6Nu3L4qKijQ+X6lUYuDAgQgJCYEgCJBIJJg7dy4mTJjwJssmIiKiCqiajQ5+/cIWBvriPxA3HJBjZ0TZVs8hel1u3bqFTz75BEOHDkXXrl1Rs2ZNnDlzBv7+/hg8eLDouU5OTpg3bx7kcjlcXFzQqVMnDBgwAO+//z4GDRqEHj16YOvWrWqvkZ2djS+//BL169dXLVFfmuKmefXq1TWOF8fv3bunlmNpaQljY+My5xEREb2t3m9gAK824t+JZ6/nYv/JbC1VRJVBVFQUAgIC0KpVK9jb20NfXx/m5uZwd3fHokWLUFBQoJYzdepUSCSS5z6KtyR6VlJSEiZOnIj69evD0NAQVlZWcHV1xaRJk9See+7cOUydOhWtW7eGhYUF9PT0UKNGDXz88ceiGzGfFhsbW2pdVatWfbkPjYjKrUgpYMWOdPwanIKCZxZdqWajg0UT7eDexFA7xRERVVIVcrMNe3t7hIaGol27dkhMTMSePXswbNgwrFu3TvS8oqIiDBgwANu2bVPdHTxv3jyMGTNGG2UTERFRBVS/lj5++NQG3/2eBKXySXxRSBpsLGRo09RIe8XROy0hIQGrV68WxcaMGYOff/4ZUqn6PbEBAQGwtbXFiBEjcPjwYVW8WrVq8PT0hJWVlVrOd999h3v37uHw4cNqS9NrkpX1eE9ZIyPN/39R3Ex/equn5+WUlKdJXl6eaFUrzoQnIqLKyt/PEqev5iI5/cnkkaVb0tCyoQFsuPctaRAREYElS5agVq1acHZ2hq2tLZKSkhAZGYmTJ09i69atOHjwoOicrlmzZhg2bJjG4z18+BBhYWEwMjJSrU70tHPnzqFr165ISUlBo0aN0KtXL8jlcly7dg3z5s3D7NmzVc8tLCxEixYtAABWVlZo3bo1jI2NceHCBaxfvx6bN2/G+vXrNW4pBAB2dnbo1q2bWtzc3LxcnxERvZxshRLTVyXj5JVctbHm9R9fOzE3kWnIJCKi0lTYs3snJyfs378fHh4ekMvl2LhxIywtLbFo0SIAjxvt/fr1w44dO1Qz2hcuXIhRo0ZpuXIiIiKqaFo1NsT4gVYIWp+qiikF4JeVKZg7ToaGtcu2HzYRAPj6+uL69evlylmzZg3c3NxEsbZt20IQBBQVFeH+/fvYvn07pk2bhn379uHgwYNwcHBQPVcQBIwfPx4LFiyAv78/AgMDYW9vj6tXr2LixIkYPXo0rl+/jiVLlqhyzp49i4ULF2Lo0KHw8PB4mbf8xsyYMQPTpk3TdhlEREQvzcRQivEDrfDtsiRVLFshYP6mNPz8uQ2X5iU1Xl5e8PLygqOjoyiekJCAzp0748iRI1ixYgUCAgJUYz4+PvDx8dF4vClTpiAsLAy+vr4wMTERjSUlJaFbt25QKBTYuXMnevbsKRo/ffq02vFatmyJb7/9Ft27d4dM9rgZp1Qq8cMPP2D69OkYMWIEPDw8YGNjo5bboEEDBAcHl+VjIKLX5GFiAb77PQn34gvVxnw6mODLPpbQkfF3ExHRi6hwy8g/zcXFBbt374aBgQEAYOnSpfjhhx9QWFgIPz8/VaNdKpViyZIlbLQTERFRibzbmGCol5kollcg4JtlSXiYqL4kI1FJ7t69i+jo6HI9cnJySjyeTCZD7dq1ERgYiFWrVuHWrVsYPXq06DmrV6/GggUL0KtXLyxbtgx169aFsbEx3NzcsGfPHtjb22PZsmW4evUqgMezj0aOHAkLCwvMmTOnzO+t+EJsSfVmZz9e/tbU1LTMOSXlafL1118jIyND9YiLiytz7URERBWNexNDdG4pXvnleJQCh8+V/DuT3l2Ojo5qjXbg8azw4u2ADh06VKZjCYKAjRs3AgCGDBmiNv7jjz8iOTkZs2fPVmu0A1C7SVRHRwenT59Gr169VI12AJBKpfj5559Rv359ZGZmYs+ePWWqj4jerPM3cvHlrAS1RrtMCgQOssKY/lZstBMRvYQKO7O9WNu2bbF582b4+vqisLAQ06dPx7Zt23Dt2jUAj0/qli1bhpEjR2q5UiIiIqrohnmbIyG1CAee2i8zI0uJKUuSsHiiHSxMuVwaPd/Fixdf27GLZx7t378f+fn5qmVC165dCwAal+Y0NTVFt27d8Ndff+HYsWNo1KgRHjx4gIsXL6Jq1aro27ev6Pnp6ekAHi8dWjzjPTw8HABQs2ZNAMCDBw801lccr1WrlipWnJOWlobs7GyN+7ZrytNEX18f+vpcaYKIiN4eo/pa4tyNXKRlPtnPaNHfaXCtb8BzTyozXV1dACjTtkDA43O7uLg4VK1aFZ07dxaNKRQKrFu3DsbGxhg+fPhL1yaRSNC0aVNER0fjv//+e+njEdGrIwgCtodnYenWNNG2egBgbiLF1JE2cKlroJ3iiIjeIhW+2Q48XkYpODgYH3/8MQCoGu0SiQTLly/Hp59+qs3yiIiIqJKQSCSYMNgKKRlFOHv9yR5l/yUV4ttlSQgaVwUGehV64R96y0kkElhZWeH+/ftIS0uDnZ0dgCfN6pL2tSyOp6WlieLx8fGIj4/XmJOeno4jR46IYi4uLgCAK1euoKCgQHVht9j58+cBAE2bNlXFLCwsULNmTdy/fx8XLlxA27ZtRTlxcXFITk5GrVq1YGYmXl2CiIjobWduIsOY/laY9meyKpaRpcSiv9Pw/afqy20TPSstLQ1BQUEAAG9v7zLlrFu3DgAwcOBA0Ux04PFWQ5mZmWjbti0MDQ2xb98+hIaGIjc3F/Xq1UO/fv1gb29frhpjYmIAAFWrVtU4npCQgB9//BGPHj2Cubk5PvjgA/Ts2bPMNw8QUfkVFApYGJKKPZHZamOO7+niF39bVLWuFO0hIqIKr9JcTR44cCAWLlwIQRAAPF5uc+XKlWy0ExERUbnoyCSYOtIGTtXFTcTrsfn45a8UFCkFLVVG9PhCZVxcHMzMzET7XRZfuDx79qzGvOJ48T7vDg4OEARB4+Pw4cMAgA8//FAVK1a7dm00bNgQCoVC4zKgW7ZsAQD06NFDFC++8Fs8XpYcIiKid0UHVyO0b24oih0+l4NjF7mcPKm7desWPvnkEwwdOhRdu3ZFzZo1cebMGfj7+2Pw4MHPzc/NzcXWrVsBaF5CvngSU5UqVeDj4wMvLy/MmzcPy5Ytw/jx4+Hk5KRagr4sjh07hnPnzkFPTw/dunXT+JwbN27gp59+wh9//IE5c+agb9++qFu3rsa94Yno5aVnFmHiwkSNjfa2LoZYNMGOjXYioldIK/9FXbNmzQvlmZqaomXLljh79iw8PT0hlUqfe6yhQ4e+0GsRERHR28vIQIpfv7RFwJwEJKYWqeLHoxRYuSsDn/lYaK84eustWrQIffv2VZv5Ex0djWHDhkEQBAwdOlQ0C8nHxwcRERGYO3cuvL29RftoLl68GEePHoWpqSm6dOny0vUFBgZi5MiRmDx5Mlq3bo0qVaoAALZt24Zdu3bByckJvXr1EuWMHTsWK1aswO+//44BAwagVatWAB5fLJ4+fTp0dHQwduzYl66NiIioshrTzwoXoh8hM+fJOr7zN6XCpZ4BTI0qzVwYegMSEhKwevVqUWzMmDH4+eefIZU+/38ru3btQkZGBho1aoTmzZurjRevhLRr1y7IZDIsWbIEffv2RU5ODhYvXow5c+Zg2LBhaNiwIZo1a1bqa8nlcowYMQIAMH78eFSrVk00rq+vjy+++AL9+/dHw4YNYWhoiKtXr+Lnn3/G3r170bVrV1y8eLHUrYby8vKQl5cnek0iKtmdB/n47vckJDx1raPYUC8zDPUyh1TK/dmJiF4lifD0VJY3RCqVQiJ5/f9Bl0gkKCwsfO2vQy9PLpfD3NwcGRkZXF6UiIjemNhHBRgzJx5ZCvHp0NSRNmjf3EhLVdHrpu3zDgcHB8TFxcHFxQVOTk4QBAH37t3DuXPnoFQq0b59e+zZswcmJiaqnNzcXHh6euLYsWOQSqVwd3eHvb09rl69imvXrkEmk2HVqlUaZy89Kzw8HB07dsSHH36IsLAwtXGlUok+ffpg+/btsLS0xIcffojk5GQcOXIEBgYGOHz4MD744AO1vHnz5iEwMBA6Ojrw9PSEnp4eDh48CIVCgYULF2L06NHl/qy0/V0RERG9SgdPZeO31SmiWNdWxpgy1FpLFVGxV3HO4evri+vXr5crZ82aNaKbKJ9WVFSE+/fvY/v27Zg2bRrs7Oxw8OBB1UpGJenZsyd2796N3377DVOmTFEb//XXX/Htt98CAGbOnInJkyeLxvv164fNmzdj0KBBWL9+fYmvU1RUBB8fH/zzzz9wc3PD0aNHy7Us/ODBg7FhwwZ89tlnWL58eYnPmzp1KqZNm6YW5/khkbqICzn4bXUKcvPF1zj0dSWYMswaHq68zkFEVB5lPUfUWrP9TZBIJCgqUr+DiyoeXkglIiJtuXQrF5MWJqLwqVMGIwMJlk6pipp2uiUnUqWl7fOO9evXY+/evTh79izi4+OhUChgZWWFZs2aYeDAgRgyZIjG8+X8/HwsXrwYISEhuH79OhQKBWxtbdGmTRtMmDBBNZv8eZ7XbAceXzxdsGAB/vrrL9y5cwfGxsbo2LEjpk2bBmdn5xKPvXv3bsyePRsXLlwAADRv3hyTJ09G9+7dy1Tbs7T9XREREb1KgiDgm6VJOHU1VxT/bZQt3BoZlpBFb8KrOOdo1qwZLl26VK6cw4cPw8PD47nP27ZtG3r37o3u3btj9+7dJT4vJSUF1apVQ1FREe7du4fq1aurPWfhwoWqFYcSExNha2srGt+3bx+8vLzw3nvv4cGDByW+1meffYY//vgD9evXx7Fjx0RbIJXF1atX0bhxY9SqVQuxsbElPk/TzPYaNWrw/JDoKUqlgLX75Fi9J0NtrIqlDD/726JujbLfDENERI9V6Ga7h4fHG5nZDkC1JyVVbLyQSkRE2rTzSCYWhKSJYrWq6WLpJDsYGnBZz7cNzzsqD35XRET0tklKK8SInx8hO/fJ5bgqljKs/K4ajA153qktFf2cQxAEmJmZITc3F9nZ2SXOIF+6dClGjRqFjh074tChQxqfs3PnTvj4+MDIyAjZ2er7OV+/fh3Ozs7Q1dVFfn6+xmN89dVXmDlzJmrUqIHIyEjUqFGj3O8pPz8f+vr60NPTEzXTn6eif1dEb5oiT4mZa1IQcUGhNtbIUQ/TPrOFlZlMQyYRET1PWc87tLJne3h4uDZeloiIiEijnu1NcO1uHkJP56hi9x4VYM76VHw3wvqN3SRIRERERG83W0sdfO5nibkbUlWxxLQirNqdjoB+VlqsjCoyiUQCKysr3L9/H2lpabCzs9P4vHXr1gFAqVsLFe/jrlAokJeXB319fdF4aurj/20+vaXR02bNmoWZM2eiSpUqCA0NfaFGO/Bk73hjY+MXyiciICG1EN//noTbDwrUxj5yN8bYAVbQ0+X1DCKi1423zBIREdE7TyKRYPwgKzi+J142/vC5HGw9nKmlqoiIiIjobeTdxhjN64sbnDsisnA/Xr1ZQgQAMTExiIuLg5mZWYnLtcfExODEiRMwNDRE7969SzxWzZo14eLiAkEQcOTIEbXx4lhxU/5pf/zxB6ZMmQILCwscOHAA9evXf8F3BGzduhUA4Orq+sLHIHqXXbubhy9nxqs12qUSYFQfC0z8mI12IqI3hc12IiIiIgAGelJM+8wGxobiP0Z/35aOqNu5JWQREREREZWPRCLBhMHW0H1qvUmlEli+PV1rNZH2LVq0CPHx8Wrx6OhoDBo0CIIgYOjQoZDJNC8HXTyrvVevXs9dXn3y5MkAgIkTJ+LRo0eq+MWLFxEUFAQA8Pf3F+Vs2bIF/v7+MDExwd69e9GsWbPnvqc//vgDN27cUItv27YNX331FQBg1KhRzz0OEYn9eyYb4+clIC1TKYqbGEowY5Qtency4wp9RERvkFb2bCd6FvdbIiKiiuLEZQW+XZYkilmZSbH862qwNuc+Z28DnndUHvyuiIjobfbHjnRsPCgXxeaMqQLXBgZaqujdVRHOORwcHBAXFwcXFxc4OTlBEATcu3cP586dg1KpRPv27bFnz54Sl3evX78+bt68iT179sDLy+u5r/fJJ59g9erVsLCwQOvWraFQKHD8+HHk5eVh5MiRWLFiheq5iYmJqFGjBvLz89GkSZMSZ6P7+PjAx8dH9bOHhweOHDmCpk2bol69elAqlbh27ZqqAT9p0iTMmjWrHJ9SxfiuiLRFEASs3pOBNXvlamM17HTwi78tatjpasgkIqIXUaH3bCciIiKqqNybGGLIR2ZYu+/JH6+pciWm/ZmMueOqQEfGu8OJiIiI6OUN6mqG/SeyRDMTl25Nw/Kvq0Im5Tnnu2b69OnYu3cvzp49iwMHDkChUMDKygqenp4YOHAghgwZAqlU8yKlp0+fxs2bN1GlShV06dKlTK+3atUqtGnTBsuXL0d4eDgkEglcXV3x+eefY9iwYaLn5uTkID8/HwBw+fJlXL58WeMxHRwcRM32kSNHwtbWFhcvXsTBgwehUChga2sLPz8/fPHFF+jcuXOZaiUiIC9fiZlrUhF+PkdtrEVDA/zwqQ1MjLiQMRGRNnBmO1UIvCuViIgqkiKlgG+WJuHMNfHy8X4dTRHQ11JLVdGrwvOOyoPfFRERve3+OZaFuRtSRbGJg63g1Ubz7GV6PXjOUXnwu6J3UUpGEb5fnoQbsflqY706mCCgjyVknBhARPTKVeiZ7Y6Ojqp/SyQS3LlzR+PYy3r22ERERERlIZNK8M0n1vD/LR4JqUWq+LbDmWjooIcPWxprsToiIiIielt85G6M7eGZuPtfgSr21+50eLxvBCMDzlAkInrX3Y7Lx7fLkpCUXiSKSyXAqL6W8PUw1VJlRERUTCvN9tjYWEgkEgiCAIlEonHsZWk6NhEREVFZmZvIMO0zW4yeE4+CwifxoPWpcHxPF7Xt9bRXHBERERG9FWQyCb7obYHJi5JUsVS5EhsPyvFpTwvtFUZERFoXeSkH04NTkJsnXpzY2ECCH/5ng5bOhlqqjIiInqa1W2RLW71eEISXfhARERG9rHo19TBugJUolpsv4McVychSKEvIIiIiIiIquxYNDfFBIwNRbPO/mUhILSwhg4iI3maCIGBTqBw/rEhWa7RXs5Zh0aSqbLQTEVUgWpnZfvjw4RcaIyIiInrTPmptgmt387AnMlsVe5BYiJmrUzDtMxtIpVxJh4iIiIhejr+fJc5cfwTl/9/PmV8gYOXOdHwz3Ea7hRER0RtVUChg3sZU7D+RrTbWpI4+pn1mAwtTmRYqIyKikmil2d6hQ4cXGiMiIiLShtH9rHA7rgDR9/NVscgoBTaFyjGoq7kWKyMiIiKit0Gtarro0c4EO49kqWJhZ3Lg2zEPDR30tVgZERG9KRlZRfhxRTKibuepjXVtZYzxA62gp8sb/omIKhqtLSNPREREVFno6Urw40gbmBmLT53+2pWBczdytVQVEREREb1NhnmZw9hQ3ERZuiWN2yUSEb0D7scXYNTsBI2N9pE+Fpg8hI12IqKKis321ywqKgoBAQFo1aoV7O3toa+vD3Nzc7i7u2PRokUoKCgoMffvv/9Gp06dYGlpCV1dXdjZ2aFXr14IDw8v9TVjY2Ph7++P2rVrQ19fHzY2NnB3d8fs2bNLzAkODoabmxtMTExgZWUFLy8vHD9+vNTXiYyMhJeXF6ysrGBiYgI3NzesWbOm1BwiIqLKqqq1Dr4bYQ3JU3/bKgXgl7+SuZ8mEREREb00C1MZPu4mXjXpakw+jlxQaKkiIiJ6E85eV2DU7Hj8lyS+tmCgJ8G0z2wwsIsZJBI22omIKio221+ziIgILFmyBPHx8XB2doafnx9atmyJixcvYsyYMfD09ER+fr5a3vjx49G/f38cPXoUzZs3h5+fH6pXr45du3ahY8eOWLFihcbX27dvHxo1aoQVK1bA2toafn5+cHV1RWxsLJYvX64xZ9y4cRg+fDiuXLmCzp07w83NDaGhoWjfvj127NihMWfr1q3o0KED9u/fj6ZNm6Jbt264desWhg0bhokTJ77w50VERFSRtWhoiBE9xBdAM7KUmPpHMvILOOOIiIiIiF6Or4cpqtmId31csT2N55pERG+pnRGZ+GpJErIV4v/O21jIsGCCHdo1M9JSZUREVFYSgWtRvVYxMTEAAEdHR1E8ISEBnTt3xpUrV7Bo0SIEBASoxqKiouDi4gILCwtERkbC2dlZNbZp0yYMGjQIRkZGiI+Ph4mJiWrsxo0baN68OUxNTbFjxw60bt1aNaZUKnH+/Hm0aNFCVEdYWBg8PT1hbW2NEydOoG7dugCAEydOwMPDA0ZGRrh79y4sLCxUOampqahduzbkcjm2bt0KPz8/1Xtq27Ytbt++jcOHD8PDw6PMn5NcLoe5uTkyMjJgZmZW5jwiIqI3TakU8MOKZByPEs8w6t7WBIGDrLRUFZUHzzsqD35XRET0Loq4kIOpfySLYp/5WGBAF/4ufF14zlF58Luit0VRkYClW9OwPTxLbaxeTT384m8DGwsdDZlERPSmlPW8Qyv/tY6IiHhjr9W+ffs39lqaPNtkL2ZnZ4cpU6ZgyJAhOHTokKjZXvz59O/fX9RoB4ABAwZgxowZiIqKwrVr1+Dm5qYaCwwMRG5uLrZu3SpqtAOAVCpVa7QDwNy5cwEA3333narRDgDu7u7w9/fHwoULsXLlSkyYMEE19ueff0Iul6NXr16qRnvxe5o1axb8/PwQFBRUrmY7ERFRZSGVSvDVUGt8MTMeD59a4u2fY1lo6KCHj1qblJJNRERERFS6ds0M0cRJH5ef2rd3/f4MdHU3hqWpTIuVERHRq5ClUOLnlck4cy1Xbax9c0N8NcwaBnpclJiIqLLQSrPdw8PjjewxIpFIUFhYcfdQ1dXVBQDo6emJ4vr6+mXKt7a2Vv07Li4OBw4cgKOjI7y8vMqUr1AocOjQIQBAnz591Mb79OmDhQsXYvfu3aJm+549e0rM8fb2hoGBAcLCwpCbmwsDA4My1UJERFSZmBhJMe0zG4yalYC8p5b0nL8pFXWq66FeTb1SsomIiIiISiaRSPBlbwt8MTNBFcvOFRD8TwbGD+RKSkREldl/yYX4dlkS7j0qUBv7+CMzfOJtDqmU+7MTEVUmWr09ShCE1/6oqNLS0hAUFATgcYP6aR07doSOjg5CQkJw7do10dimTZtw+fJldOjQAXXq1FHFw8PDoVQq0bp1axQWFuLvv//G2LFjERAQgN9//x1paWlqNURHRyMvLw+2traoXr262rirqyuAx8vaP+3SpUui8afp6emhcePGyM3Nxc2bN8vyURAREVVKju/pYcJg8cXOgkJg6h9JyMgq0lJVRERERPQ2qF9LH55u4n169xzLwt3/8rVUERERvazLt3Mxala8WqNdVwf4epg1RvSwYKOdiKgS0srM9po1a76Rme0Vya1btzB9+nQolUokJCTg+PHjyMrKgr+/PwYPHix6rpOTE+bNm4exY8fCxcUF7dq1Q5UqVXDr1i1cuHABPXr0wF9//SXKKW7Km5iYoF27djh58qRo/Ntvv8WWLVvQsWNHVez+/fsAoLHRDgDGxsawsLBAWloaMjMzYWpqCrlcjoyMjFLzqlevjrNnz+LevXto2rRpOT4lIiKiyqWzmzGux+aJ9liLTynCr8Ep+PVLW8j4RzIRERERvaD/9bJAxAWFaiUlpQAs35aO3wKqaLkyIiIqr4MnsxC0IRUFzyzEa2EixU+f26JxnbKtdktERBWPVprtsbGx2nhZrUpISMDq1atFsTFjxuDnn3+GVKq+wEBAQABsbW0xYsQIHD58WBWvVq0aPD09YWUlnklXPHP9zz//hImJCTZs2IBu3bohKSkJP//8M9atWwdfX19cvXoV7733HgAgK+txY8DISHyn9NOMjY2Rnp6uarYX55SWZ2xsDADIzMws8bh5eXnIy3uy95hcLi/xuURERBWZv58lbsUV4MqdJ7/XzlzLxdq9Gfiku4X2CiMiIiKiSs3WUgf9PU2xZu+Tayanr+Xi9FUF3BoZarEyIiIqK6VSwMpdGdh4UP36t0M1Xfz6pS2qWmulTUNERK+IVpeRrwx8fX3RoEGDcj1Onz6tdpy2bdtCEAQUFhYiJiYGQUFBCA4ORosWLdRuPhAEAePGjcOAAQMwdOhQ3Lx5E1lZWTh16hScnJwwevRoBAQEiHKUSiUAoLCwEMuXL8fAgQNhaWmJevXqYe3atWjZsiUyMjKwdOnS1/ZZlceMGTNgbm6uetSoUUPbJREREb0QXR0JfvjUGpZm4tOqNXvlOHFZoaWqiIiIiOht0L+zGazNZaLYsm3pKCqquFsnEhHRY4o8Jab9mayx0f5BIwMsmmjHRjsR0VuAzfbnuHv3LqKjo8v1yMnJKfF4MpkMtWvXRmBgIFatWoVbt25h9OjRouesXr0aCxYsQK9evbBs2TLUrVsXxsbGcHNzw549e2Bvb49ly5bh6tWrqhwTExPV/+3bt6/a6w4fPhwAcOTIEbWc0urNzs4GAJiamopySst7NkeTr7/+GhkZGapHXFxcic8lIiKq6GwsdPDDpzZ4drGaGcHJeJhUoDmJiIiIiOg5DA2kGNHTXBS796gAeyKzSsggIqKKICm9EOPmJuDoRfWb8Ht3MsUvX9jC2JDtGSKitwH/a/4cFy9ehCAI5Xp4eHiU6di+vr4wMTHB/v37kZ+fr4qvXbsWANCnTx+1HFNTU3Tr1g2CIODYsWOqeK1atQAANWvWhESivj+sg4MDACAxMVEVq1mzJgDgwYMHGuvLzs5Geno6LC0tVY1zMzMzmJubl5pXHC+uSRN9fX2YmZmJHkRERJWZS10DfO5rIYplKQRM/SMZuflK7RRFRERERJVe1w+M4VRDVxQL/icDWQqeYxIRVUR3HuRj1KwE3IoT33wvkwLjB1piVB9LyKTq1/CJiKhyYrNdiyQSCaysrFBYWKjacx140qwubmo/qzj+dE7z5s3VYk9LTU0FIJ6ZXr9+fejr6yMpKQkPHz5Uyzl//jwAoGnTpqK4i4uLaPxpBQUFuHLlCgwMDFCvXj2NtRAREb2t+nQyhYerkSh250EB5m1IhSBwqU8iIiIiKj+pVIIv/CxFsfQsJTbsz9BSRUREVJJTVxUYE5SA5PQiUdzEUIKZAVXQo13Jq8ESEVHlxGa7FsXExCAuLg5mZmawsbFRxatWrQoAOHv2rMa84njxbHUAaN26NaytrREfH4/o6Gi1nOLl44ub8gBgaGiITp06AQA2b96slrNlyxYAQI8ePURxb29v0fjT/vnnH+Tm5qJz584wMDDQWD8REdHbSiKRYNLHVqhVVbznWujpHOw6yqU+iYiIiOjFNK9vgDZNDUWxrYcz8Si5UEsVERHRs3ZGZOLbpUlQ5Ilvtn/PVgeLJ1eFawNeLyciehux2f6aLVq0CPHx8Wrx6OhoDBo0CIIgYOjQoZDJZKoxHx8fAMDcuXNx+vRpUd7ixYtx9OhRmJqaokuXLqq4jo4OAgMDIQgCRo0aBblcrhoLCwtDcHAwJBIJPv/8c9HxAgMDAQC//PILbt26pYqfOHECy5cvh4WFBT799FNRzv/+9z+YmZlh586d2LZtmyqemJiIyZMnAwAmTJhQps+HiIjobWNoIMW0z2xhZCBeEm7J5jRcu5unpaqIiIiIqLL73M8Csqeu5BUUAn/sSNdaPURE9FiRUsCyrWlYsCkNymcWtWtSRx+LJ9mhpp2u5mQiIqr0JALXNH2tHBwcEBcXBxcXFzg5OUEQBNy7dw/nzp2DUqlE+/btsWfPHtHy7rm5ufD09MSxY8cglUrh7u4Oe3t7XL16FdeuXYNMJsOqVaswZMgQ0WsVFBTAy8sLYWFhsLOzQ6tWrZCcnIyTJ0+iqKgI06dPxzfffKNW47hx47BgwQIYGRnB09MT+fn5CA0NhSAI2LJli6r5/7StW7eiX79+qj3qra2tERYWhvT0dAQGBiIoKKhcn5NcLoe5uTkyMjK4fzsREb0VIi7kYOofyaKYjYUMy7+uCktTWQlZ9CbwvKPy4HdFREQktmRLGrYeyhTFFk6wQ+M6+lqq6O3Ac47Kg98VVTSKPCV+DU5B5CWF2tiHLY0w6WNr6Olyf3YiosqorOcdbLa/ZuvXr8fevXtx9uxZxMfHQ6FQwMrKCs2aNcPAgQMxZMgQSKXqCwzk5+dj8eLFCAkJwfXr16FQKGBra4s2bdpgwoQJaNWqlcbXKygowLx587BmzRrcuXMHenp6aNGiBcaPH4/u3buXWGdwcDAWL16M69evQ09PD61atcL333+P1q1bl5gTGRmJX375BSdPnkR+fj6cnZ0REBCAYcOGlftz4okyERG9jVZsT8OmUPHF0Gb19DF7TBXIpPxjW1t43lF58LsiIiISk2cXYciPj5CZo1TFGjjoYfFEO0h5fvnCeM5RefC7oookNaMI3y5LQvT9fLWxoV5mGOZtDomE/20mIqqs2GynSoUnykRE9DYqKhIwaVEiLt4ULx/v72eBfp35+05beN5RefC7IiIiUrf1kBxLtqSLYt8Ot8aHLY21U9BbgOcclQe/K6oo7v6Xj6+XJiExtUgU15EBEwZboWsrkxIyiYiosijreQf3bCciIiJ6TWQyCb7/1AY2FuJl41fuSsfd/9TvfCciIiIiep5eHUxRvYqOKPbHjnTk5itLyCAiolfpzDUFxsxJUGu0mxhKMDOgChvtRETvGDbbiYiIiF4jS1MZvv3EGk+vHFdQCPy2OgWFRVxgiIiIiIjKR0cmwed+FqJYYloRtvybqTmBiIhemX+OZeHrpUnIzhX/PV/NWobFk6qieX0DLVVGRETawmY7ERER0WvmUs8AvTuaimK34gqwbl+GlioiIiIiosqsdRNDNKunL4ptOChHakZRCRlERPQylEoBK3akY+6GVCifWUjEubYelkyuippVdbVTHBERaRWb7URERERvwKc9zVGrqni5z3X75Yi+l1dCBhERERGRZhKJBF/2thStnpSbJ+Cv3elaq4mI6G2Vl6/ETyuTsemgXG2sg6sRgsZWgYWpTEMmERG9C9hsJyIiInoD9PWk+GqYNaRPnX0plY+Xk8/j/ppEREREVE5ONfTQrZWxKLbvRDbuPMjXUkVERG+ftMwiBM5PRMQFhdrYoK5m+H6ENfT12GYhInqX8bcAERER0RtSv5Y+Pu5mJordiy/EX7u5nDwRERERld+InhYw0H8yvV0QgKVb0yAIQilZRERUFvceFSBgVjyux4pvYpJJgYmDrfC/XhaQSiUlZBMR0buCzXYiIiKiN+jjj8xRt4Z4H7cthzJx6VaulioiIiIiosrK2lyGgV3EN3NeiM7DicvqMzCpYouKikJAQABatWoFe3t76Ovrw9zcHO7u7li0aBEKCgrUcqZOnQqJRPLcR0REhMbXTEpKwsSJE1G/fn0YGhrCysoKrq6umDRpktpzg4ODS32NAQMGlPjerl69ir59+8LW1haGhoZo0qQJ5s+fD+WzG18TVSDno3MRMCcej1KKRHFjAwl+C6gCrzYmWqqMiIgqGp3nP+XN6tSp00sfQ09PD+bm5rCxsYGLiwtat26Nxo0bv4LqiIiIiF6OjkyCr4ZZw/+3eBQUPo4JAjBrTQr++LYajAx4LyQRERERlV3fD03xz9EsJKU/aQj9vi0dbo0MoSPjjMvKIiIiAkuWLEGtWrXg7OwMW1tbJCUlITIyEidPnsTWrVtx8OBB6OnpqXKaNWuGYcOGaTzew4cPERYWBiMjI7i6uqqNnzt3Dl27dkVKSgoaNWqEXr16QS6X49q1a5g3bx5mz56t8bguLi5o1qyZWvyDDz7Q+PwTJ07gww8/hEKhgJubGxwcHBAREYHx48fj+PHjCAkJgUTC/51SxbLvRBbmrk9F0TP3g9hZyfDrl7aoba+nOZGIiN5JFa7ZHh4e/lpOsFxdXfHDDz+gR48er/zYREREROVR214PI3pYYPn2dFXsUUoRft+WjsBBVtorjIiIiIgqHQM9Kf7XywIzVqeoYg8SC7ErIgt+HU21WBmVh5eXF7y8vODo6CiKJyQkoHPnzjhy5AhWrFiBgIAA1ZiPjw98fHw0Hm/KlCkICwuDr68vTEzEM3CTkpLQrVs3KBQK7Ny5Ez179hSNnz59usQ6fXx8MHXq1DK9p4KCAgwePBgKhQJz587F+PHjAQBZWVno0qULNm/eDC8vL3zyySdlOh7R6yYIAv7anYH1++VqY/Vr6WG6vy2szGVaqIyIiCqyCjl1ShAE0aOk+LPjpT3n3Llz8PHxEZ2QEhEREWlLnw9N0cRJXxT751gWTl3lkp9vuxdZIrTY33//jU6dOsHS0hK6urqws7NDr169EB4eXuprxsbGwt/fH7Vr14a+vj5sbGzg7u5e4owl4PFSoW5ubjAxMYGVlRW8vLxw/PjxUl8nMjISXl5esLKygomJCdzc3LBmzZpSc4iIiOjlfdjSCPVriWdartmbgcwcLtNdWTg6Oqo12gHAzs4OU6ZMAQAcOnSoTMcSBAEbN24EAAwZMkRt/Mcff0RycjJmz56t1mgHADc3t/KUXqLt27fj7t27cHFxUTXaAcDExASLFy8GAAQFBb2S1yJ6WfkFAqavStHYaG/rYoh546uw0U5ERBpVuJntq1atAgBkZmbip59+QkpKCgRBgIODAzp06ICGDRvC3NwcUqkUaWlpuHHjBo4cOYK7d+8CAGxtbfH9999DV1cXiYmJOHPmDA4cOICCggIIgoBly5ahatWq+O6777T5NomIiOgdJ5NKMGWoNf43/RFy857cPDhnXSpWflcVZsb8I/5t9SJLhALA+PHjMX/+fOjo6KBdu3awtbXF7du3sWvXLuzatQvLly/HZ599pvZ6+/btQ58+faBQKODq6opWrVohJSUFly9fxvLlyzXuyTlu3DgsWLAAhoaG6NKlC3JzcxEaGoqDBw9iy5YtGmdQbd26Ff3794dSqUT79u1hY2ODf//9F8OGDUNUVBTmzJnzyj5DIiIiEpNKJfiytwXGzk1UxeTZSqzdm4Ev+1hqsTJ6FXR1dQFA7fywJOHh4YiLi0PVqlXRuXNn0ZhCocC6detgbGyM4cOHv/Jan7Znzx4AQJ8+fdTGXF1d4ejoiCtXriA2NhYODg6vtRai0mRkFeH75cm4cidPbaxfZ1N85mMBqZTbHRARkWYVrtk+bNgwxMbGwtPTEykpKahbty4WLFiArl27lpoXGhqKsWPH4saNG1iwYAEOHjyI2rVrA3i8R9GIESMQGhoKQRDw66+/4tNPP0W1atXexFsiIiIi0sjeRgdf+Flg3sY0VSwlowgLQ9Lw3QgbLVZGr9OLLBEaFRWF+fPnw8LCApGRkXB2dlaNbdq0CYMGDUJgYCAGDRokWib0xo0b8PPzg6mpKUJDQ9G6dWvVmFKpxPnz59XqCwsLw4IFC2BtbY0TJ06gbt26AB7vt+nh4YHhw4fDw8MDFhYWqpzU1FSMGDECRUVF2Lp1K/z8/FTvqW3btggKCkL37t3h4eHxUp8dERERlayJkwHaNzdExIUnKyXtOJKJnu1NUL2KrhYro5eRlpammv3t7e1dppx169YBAAYOHAiZTHwT79mzZ5GZmYm2bdvC0NAQ+/btQ2hoKHJzc1GvXj3069cP9vb2JR773LlzmDRpEuRyOapWrYpOnTqhQ4cOGp976dIlANC4Z3xxPCYmBlFRUWy2k9bEJRTg66VJ+C+pUBSXSoEx/SzRsz234yAiotJVuGXk8/Pz0bt3b9y5cwcuLi44efLkcxvtAODp6YmTJ0/CxcUFd+7cQe/evZGX9/hOtPfeew979uzBBx98AADIy8tTzaAnIiIi0qbubU3g5mwgih06m4Pw8zlaqohetxdZIjQiIgIA0L9/f1GjHQAGDBiAJk2aIDs7G9euXRONBQYGIjc3F8HBwaJGOwBIpVK0aNFCrY65c+cCAL777jtVox0A3N3d4e/vj/T0dKxcuVKU8+eff0Iul6NXr16qRnvxe5o1axYALhFKRET0Jnzmawndp6bWFBYBy7ena60eKr9bt27hk08+wdChQ9G1a1fUrFkTZ86cgb+/PwYPHvzc/NzcXGzduhWA5iXki88Xq1SpAh8fH3h5eWHevHlYtmwZxo8fDycnJ9US9Jr8888/mDNnDlasWIGffvoJHh4e8PDwQEJCgtpz79+/DwCoXr26xmMVx+/du/fc90X0Oly6lYuA2QlqjXYjAwl+/cKWjXYiIiqTCtds37hxIy5cuACJRII//vhDNGPmeczMzPDnn38CeHzn5IYNG1RjOjo6mDFjhurnsu5xRERERPQ6SSQSTPjYCiaG4iXp5m9MRWpGkZaqIm0paYlQfX39MuVbW1ur/h0XF4cDBw7A0dERXl5eZcpXKBSq82RNy30Wx3bv3i2Kl7ZEqLe3NwwMDBAWFobc3Nwy1UFEREQvxt5GB34e4uZQ5CUFLt7k7+DKIiEhAatXr8batWtx8OBBZGVlYcyYMZg5cyak0udfyt21axcyMjLQqFEjNG/eXG08LS1N9bz9+/djyZIlSExMRGxsLCZOnAiFQoFhw4bh4sWLorxq1aph6tSpuHDhAjIyMhAfH49du3ahQYMGOHLkCLp3746iIvHfL1lZWQAAIyMjjbUaGxsDeLydaEny8vIgl8tFD6JXIex0NiYtTERmjlIUt7WQYUGgHdwaGWqpMiIiqmwqXLN97dq1AB7P+Hn//ffLnf/++++jTp06AJ4smVSseLlLQRBw8+bNly+WiIiI6BWwtdDB2AFWopg8W4k561MgCEIJWfS2KW2J0I4dO0JHRwchISFqs9c3bdqEy5cvo0OHDqrzYODxXp1KpRKtW7dGYWEh/v77b4wdOxYBAQH4/fffVRdanxYdHY28vDzY2tpqnIFUvARoVFSUKF7aEqF6enpo3LgxcnNzeQ5ORET0Bgz+yBzmJuJLfku3pkGp5Hnl6+Tr64sGDRqU63H69Gm147Rt2xaCIKCwsBAxMTEICgpCcHAwWrRogdjY2OfWUXw9VNOsduDxVkIAUFhYiJ9++glffvklbG1tUatWLcyePRt9+/ZFQUEBZs+eLcrr2rUrfvzxRzRr1gxmZmaws7NDjx49cObMGdSrVw9nz57F33//Xc5P7flmzJgBc3Nz1aNGjRqv/DXo3SIIAtbty8CvwSkofOb+9ro1dLFksh3qVNfTnExERKRBhWu237x5ExKJBDVr1nzhY9SsWbPEhnr9+vUBACkpKS98fCIiIqJXrVMLI7RvLr5z/uSVXOw/ka2liuh1K88SoU5OTpg3bx7kcjlcXFzQqVMnDBgwAO+//z4GDRqEHj16qJYLLVbclDcxMUG7du3Qv39/LFy4EEuWLMEXX3wBJycnHD58WJTzvKU+jY2NYWFhgbS0NNUMJLlcjoyMjFLzyrpEKGcuERERvTwTQyk+6W4uit2OK8ARblP0Wt29exfR0dHleuTklPydyGQy1K5dG4GBgVi1ahVu3bqF0aNHl1pDSkoK9u/fD6lUWuKS8yYmJqp/Dx8+XG28OHbkyJGyvG2YmJhgzJgxAIADBw5ofK2S3md29uO/dUxNS16q++uvv0ZGRobqERcXV6a6iDQpKhIwd0Mq/tqdoTbm3sQQ8wPtYGOhoyGTiIioZBWu2Z6UlAQAqgt2L6I4Nzk5WW2s+OStsLBQbYyIiIhIWyQSCcYPtIKlmfj0bMmWNMSn8LzlbVTeJUIDAgKwYcMG6Onp4fDhwwgJCcH58+dRtWpVeHp6wspKvDpC8cz1P//8Ezdu3MCGDRuQmpqK6OhofPzxx0hNTYWvry8ePnyoynneUp+A+nKfxTml5ZVliVCAM5eIiIhele5tTFCrqrhhtP6AnKsmvUYXL16EIAjlenh4eJTp2L6+vjAxMcH+/fuRn59f4vNCQkJQUFCADh06lHgTZK1atQA8Pm+ztbVVG3dwcAAAJCYmlqk2AKhbty4A4NGjR6J48WSqBw8eaMwrjhfXpIm+vj7MzMxED6IXochV4rvfk7AnUv2Gdj8PE/z0uQ0M9Stcu4SIiCqBCvfbw8bGBoIg4PLly0hNTS13fkpKCqKioiCRSER7VhYrvhj49F2cRERERBWBuYkMEwaJG6Y5uQJmrUnhsp8ViDaWCBUEAePGjcOAAQMwdOhQ3Lx5E1lZWTh16hScnJwwevRoBAQEiHKeXiJ0+fLlGDhwICwtLVGvXj2sXbsWLVu2REZGBpYuXfraPqvy4swlIiKiV0Mmk2B4DwtRLOZhAU5cVminIHopEokEVlZWKCws1LgVULHnLSEPQLWPu0KhQF5entp48fXY8lw7La6p+AbLYi4uLgCA8+fPa8wrjjdt2rTMr0X0IlIzijB+fiJOXc0VxSUS4Ms+FgjoZwWZVKKl6oiIqLKrcM324n0eCwsL8fXXX5c7/9tvv1XNWte0Z+SdO3cgkUhKvLuTiIiISJtaNzVCN3fxRaqLt/KwPbz0GcH05mhjidDVq1djwYIF6NWrF5YtW4a6devC2NgYbm5u2LNnD+zt7bFs2TJcvXpVlVN8gdTExAR9+/ZVe11NS4Q+b6lPQH25z6cvxL7MEqEAZy4RERG9Sm1dDNVnt+/n7PbKKCYmBnFxcTAzM4ONjU2Jzzlx4gQMDQ3Ru3fvEo9Vs2ZNuLi4QBAEjUvFF8eKm/JlUbyd0bPXYr29vQEAW7ZsUcu5cOECYmJi0LhxY9VseqLX4X58AQLmxOPmffGqEHq6Evz4Pxv06cS/OYiI6OVUuGb7xx9/rPr3n3/+iTFjxkCheP5dt7m5uRg3bhxWrFihij17F+edO3dUy9Q3btz4FVVMRERE9GqN6mOJKlYyUeyPnRm4H1+gpYroadpYInTt2rUAgD59+qjlmJqaolu3bhAEAceOHVPFi5fjrFmzJiQS9VkampYIfd5Sn9nZ2UhPT4elpaWqcW5mZgZzc/NS88qyRCgRERG9WlKpBAO7ivduvx6bjwvR6rOZSfsWLVqE+Ph4tXh0dDQGDRoEQRAwdOhQyGQyDdlPZrX36tXruTcsTp48GQAwceJE0dLvFy9eRFBQEADA399flDNjxgy1LTsLCgowbdo0bN68GYaGhmp7wPv6+qJ27dq4dOkS5s2bp4pnZ2dj1KhRAIAJEyaUWivRy4i6nYvRcxIQn1IkipsZSzFnTBW0b17y9llERERlVeGa7X379oWHh4fqLtslS5bAyckJU6ZMwf79+xEbG4v09HSkp6fj3r17OHDgAL766is4OTlh0aJFAB4vrdShQwe1GTzbtm1T/btDhw5v7k0RERERlYOxoRRThoi3w8kvEPDbmhQUFXEm0tuspCVCi5vVxU3tZxXHn84pno1U0lKjmpYIrV+/PvT19ZGUlCTay71YSUt9lrZEaEFBAa5cuQIDAwPUq1dPYy1ERET0enzYwgjVrMXN2XX7M7RUDZUmKCgI7733HlxdXdGvXz/07dsXbm5ucHZ2xqlTp9C+fXvMmDGjxPz169cDKH0J+WKDBg3CsGHDcPnyZTg7O8Pb2xudOnVCq1atkJqaipEjR6pdV/3mm29QvXp1tG3bFgMHDoS3tzccHBwwdepUGBgYYN26dXjvvfdEObq6uli3bh0MDQ0RGBiIVq1aoX///qhbty5OnDiBPn36YNiwYS/waRE9X/i5bExamIjMHKUoXs1GB4sm2qFxHX0tVUZERG+bCtdsB4DNmzejWbNmqob7o0ePMGfOHHh7e6NOnTqwtraGtbU1HB0d4eXlhdmzZ+O///5T5Tdp0gSbN29WO+6OHTtgZ2eHqlWrwtfX9429HyIiIqLyal7fAH4e4n0Sb8TmY8NBuZYqojehpCVCq1atCgA4e/asxrzi+NNLcLZu3RrW1taIj49HdHS0Wo6mJUINDQ3RqVMnANB4Pl28BGiPHj1E8dKWCP3nn3+Qm5uLzp07w8DAQGP9RERE9HrIZBIM6CKe5XzxZh6u3OHs9opm+vTpGDBgALKzs3HgwAHs3LkT9+/fh6enJ4KDg3H48OES91E/ffo0bt68iSpVqqBLly5ler1Vq1ZhxYoVqFOnDsLDw3H69Gm4uroiODhYtHJosR9++AHt27dHXFwcdu7ciUOHDsHIyAiff/45Ll68CD8/P42v07p1a5w5cwa9e/fG7du3sWvXLlhZWWHu3LkICQnRuAIT0csQBAF/h8nx08oUFBSKx+rX0sPiSXaoYaerneKIiOitJBEq6EZNWVlZmDx5MpYvX65qukskErV9pZ6OSSQSjBw5ErNnz37ufpBUscjlcpibmyMjI4N7cxIREf2/3HwlPvs1Hg8Sn1whkEmBpVOqom4NPS1WVrlp+7xj0aJF6Nu3r6qBXiw6OhrDhg3DqVOnEBAQoFq1CQDmzZuHwMBAmJiY4N9//4Wbm5tqbPHixRg9ejRMTU0RGxsLKysr1divv/6Kb7/9Fh9++CG2bdumer9hYWHw8vJCYWEhTp48KTpeWFgYPD09YW1tjRMnTqBu3boAgBMnTqBjx44wNDTE3bt3YWFhocpJTU1F7dq1IZfLsXXrVtXF1sTERLRp0wa3b9/G4cOHy7ycfjFtf1dERERvg/wCAYN/+A8pGU+WUf6gkQFmjKqixaoqFp5zVB78rqg0RUoBS7ekYXt4ltqYexNDfDfCGob6FXL+IRERVUBlPe+osM32Yjdv3sSff/6JvXv34saNG1Aqxcu+SCQS1K9fH15eXvjf//6HBg0aaKlSehk8USYiItLs+t08jJ6TAOVTZ2y17XWxbEpV6OlyFsiL0PZ5h4ODA+Li4uDi4gInJycIgoB79+7h3LlzUCqVaN++Pfbs2SOauZSbmwtPT08cO3YMUqkU7u7usLe3x9WrV3Ht2jXIZDKsWrVKbdnQgoICeHl5ISwsDHZ2dmjVqhWSk5Nx8uRJFBUVYfr06fjmm2/Uahw3bhwWLFgAIyMjeHp6Ij8/H6GhoRAEAVu2bIGPj49aztatW9GvXz/VHvXW1tYICwtDeno6AgMDVft/loe2vysiIqK3xeZ/5Vi2NV0UW/41b+AsxnOOyoPfFZUkN1+JX1el4NglhdpYz3YmGN3PEjIZ/4YmIqKye2ua7U/Lzs7GgwcPkJ6eDuDx3pTVq1cvcQklqjx4okxERFSylbvSsX6/ePn4AZ6m+MzXUksVVW7aPu9Yv3499u7di7NnzyI+Ph4KhQJWVlZo1qwZBg4ciCFDhkAqVZ9tkZ+fj8WLFyMkJATXr1+HQqGAra0t2rRpgwkTJqBVq1YaX6+goADz5s3DmjVrcOfOHejp6aFFixYYP348unfvXmKdwcHBWLx4Ma5fvw49PT20atUK33//PVq3bl1iTmRkJH755RecPHkS+fn5cHZ2RkBAwAvvxant74qIiOhtochTYtD3/yEj68kklvbNDTF1pK0Wq6o4eM5RefC7Ik3SM4vw3e9JuHY3X21spI8FBniacssCIiIqt7ey2U5vL54oExERlaygUMCoWfG4/aBAFZNIgAWBdmhcR1+LlVVOPO+oPPhdERERvTrr9mXgr90Zqp8lEuCv76qhVjXuXcxzjsqD3xU962FiAb5akoSHSeIN2nVkwJSh1viwpbGWKiMiosqurOcd3KCEiIiIqILT1ZHgq2HW0NV5EhME4Lc1KVDkKUtOJCIiIiL6fz4dTGFs8GRmpyAAGw/KS8kgIqrYrt/NQ8CcBLVGu7GhBLNGV2GjnYiI3gg224mIiIgqAcf39PBJdwtR7L+kQqzYnq6VeoiIiIiocjExksLHw1QUCzuTjUfJhSVkEBFVXJGXchA4P1G0PQYAVLGUYeEEOzSrZ6ClyoiI6F2j8/ynaN+dO3ewa9cunDp1Crdv30ZaWhry8vJgYWEBW1tbuLq6ol27dujevTt0dCrFWyIiIiIqt36dTXE8KgdXY57sQ7czIgttXAzRoqGhFisjIiIiosqgTydTbD2Uidz8x7tKKpXAplA5xg+00nJlRERlt+NIJhb9nYZnN8h1qq6LX7+0hY0FewRERPTmVOiZ7bdv34a3tzfq16+PiRMnYvPmzbhw4QJiY2Px33//4fr164iIiMD8+fPRu3dv1KhRA/Pnz9d22URERESvhUwqwVdDrWGgJxHFZ61NRVYOl5MnIiIiotKZm8jQva2JKLb/RBaS0zm7nYgqPqVSwPJtaVgYot5ob+lsgPmBdmy0ExHRG1dhm+1///03mjVrhv3790OpVEIQBNEDgNrPCQkJmDBhAjw8PJCZmanN8omIiIhei/eq6OJzXwtRLDm9CIv+TtVOQURERERUqfTrbArdp3pRBYXA5n95HY2IKrb8AgHTV6UgJEz9v1cfuRtj+he2MDKosO0OIiJ6i1XI27z27duHjz/+GIWFhZBIHs/cMjc3R+fOndGkSRPY2NhAT08PmZmZuHPnDk6dOoVz584BeNyAP3r0KLp3745Dhw5BJpNp860QERERvXI925vg2CUFzt3IVcVCT+egbbMctGtmpMXKiIiIiKiis7HQQbdWJth9LEsV2300C4O6msHchNfRiKjikWcX4YflyYi6nac2NszbHEO9zFR9BCIiojetwjXbc3Jy8Nlnn6ka7ZaWlvjll18wfPhw6Ovrl5h348YNTJ48Gf/88w8EQcCxY8ewePFijB079g1WT0RERPT6SSQSTB5ihRG/PEK24snaefM2pKJxHX1YmvIiKRERERGVbEAXM+w5ngXl/+9ElJsvYMuhTHza00KrdRERPSs+pRBfL0nEvXjxdhcyKTBhsBW6uZuUkElERPRmVLh1VdasWYOHDx9CIpHA3t4eJ06cgL+/f6mNdgBo0KABdu3ahSlTpgB4PMN95syZb6JkIiIiojfO1lIHo/tZiWLpWUrM3ZCq2mKHiIiIiEiTajY6+LCFeEWkHUcykaVQaqkiIiJ1N+/nI2B2vFqj3chAgl+/tGWjnYiIKoQK12zfvXu36t/Lly9H3bp1y5U/Y8YMfPDBBwAe7+F++vTpV1ofERERUUXh6WaEti6GoljkJQVCT+doqSIiIiIiqiwGdTXH06suZysE7DzCvduJqGI4dVWBcfMSkCoX3wRkbS7D/PF2aOlsWEImERHRm1Xhmu1Xr14FAFSrVg1eXl4vdIwRI0aoHY+IiIjobSORSBA4yAoWJuJTut+3piErh7OSiIiIiKhktarpol0zcbNqy6FMKPJ4HklE2rU3MgvfLktCbp541TaHarpYPMkOTjX0tFQZERGRugrXbE9MTIREIin3jPan1atXT/XvpKSkV1EWERERUYVkYSpD4CD15eSD92RoqSIiIiIiqiwGdzMX/ZyRpcSeyCwtVUNE7zpBELB2XwbmrE+F8pn7fprV08fCCXaws9LRTnFEREQlqHDNdgMDAwBAdnb2Cx8jJ+fJ0qnP2+v9dYuKikJAQABatWoFe3t76Ovrw9zcHO7u7li0aBEKCgpKzP3777/RqVMnWFpaQldXF3Z2dujVqxfCw8NLzAkNDYW3tzdsbW2hq6sLa2trdOnSBdu3by8xR6FQ4IcffkC9evVgYGAAe3t7jBgxAg8fPiz1vQUHB8PNzQ0mJiawsrKCl5cXjh8//tzPhIiIiF6tts3Ul5PfcSQTd//L11JFRERERFQZ1K2hhw8aGYhiIaGZyC8QSsggIno9ipQC5m9Kw6rd6jeOd25phN9GVYGJUYVrZxAREVW8Znu1atUgCAKuXLkCuVz+Qsc4duyY6HjaFBERgSVLliA+Ph7Ozs7w8/NDy5YtcfHiRYwZMwaenp7Iz1e/ED5+/Hj0798fR48eRfPmzeHn54fq1atj165d6NixI1asWKGWM3/+fHTp0gX79u1DvXr10Lt3bzRo0ABhYWHw8/PDt99+q5aTm5uLTp064eeff0ZWVhZ69eqFGjVqYNWqVWjevDliYmI0vq9x48Zh+PDhuHLlCjp37gw3NzeEhoaiffv22LFjx0t/bkRERFQ+X/S2hO5TN/grlcDizWkQBF4oJSIiIqKSPTu7PSWjCAdPvfgkGCKi8srLV2LqH8nYfVR9ZY2BXczw1TBr6OlKtFAZERHR81W4Znv79u0BAHl5efj111/LnZ+QkIDly5erfm7Xrt0rq+1FeHl54c6dO4iNjUVYWBg2btyIsLAwxMbGonHjxjhy5Iha4zwqKgrz58+HhYUFLl26hEOHDiEkJATnzp3Dxo0bH+/PGhiIrKwnJx9JSUn46quvoKuri8OHDyMyMhKbNm1CZGQkwsPDoa+vjxkzZqg1z3/55RecPHkS7u7uuHnzJkJCQnDq1CkEBQUhKSkJI0aMUHtPYWFhWLBgAaytrXHp0iXs2LED+/fvR0REBGQyGYYPH4709PTX8nkSERGRZtVsdDDA00wUuxCdh6MXFVqqiIiIiIgqg8Z19NGsrnhlyI0H5Sgq4k2bRPT6ybOLMGlREiIvif92lUiA0f0sMdLHAlIpG+1ERFRxVbhm+4ABA1T/njNnDmbPnl3m3AcPHqBr165ITU2FRCJBu3bttD6z3dHREY6OjmpxOzs7TJkyBQBw6NAh0VhERAQAoH///nB2dhaNDRgwAE2aNEF2djauXbumip86dQp5eXno1KkTOnToIMpp3749unbtCkEQcPbsWVU8Pz8fixcvBgAsWbIEJiYmqrHAwEA0bdoUR44cwblz50THmzt3LgDgu+++Q926dVVxd3d3+Pv7Iz09HStXrnzOJ0NERESv2sCuZqhiKRPFlm5NQ26+soQMIiIiIiJg8Efi2e2Pkgtx6GxOCc8mIno1ElMLMXZuIq7cyRPFdXWAHz61ga+HqZYqIyIiKrsK12zv0KEDvL29IQgClEolvvrqK7i5uWHdunVITk5We75SqcT58+cxadIkNGzYEJcvXwYASCQSzJo1602XXy66uroAAD09PVG8rPvMW1tbv1ROZGQkMjIyUKdOHTRv3lztuX369AEA7N69WxVTKBSqmwOKx5+XQ0RERG+GgZ4U/r0tRbHE1CKEhGZqqSIiIiIiqgxc6+ujgYP4+tSGAxlQKjm7nYhej7v/5SNgTgLuPSoQxY0NJJgZUAUdXI20VBkREVH5VLhmOwAEBwejYcOGqp/PnTuHYcOGwc7ODtWqVUPjxo3h6uqKunXrwtjYGC1btsTcuXORnZ2t2pd03rx5cHNz09ZbeK60tDQEBQUBALy9vUVjHTt2hI6ODkJCQkSz1wFg06ZNuHz5Mjp06IA6deqo4m5ubrCwsMChQ4dw5MgRUU5ERAQOHDiAunXripbVv3TpEgDA1dVVY43F8aioKFUsOjoaeXl5sLW1RfXq1cuUQ0RERG9Oh+aGGpcBjU8p1FJFRERERFTRSSQSDO4m3pLoXnwhjl3ilkRE9OpdupWLsUEJSE4vEsWtzWWYH2iHZvUMtFQZERFR+VXIZru1tTUOHz4MLy8vCIKgaqALgoCEhARcv34dly5dQkxMDPLy8lTjAGBubo7Vq1dj9OjR2ipfo1u3buGTTz7B0KFD0bVrV9SsWRNnzpyBv78/Bg8eLHquk5MT5s2bB7lcDhcXF3Tq1AkDBgzA+++/j0GDBqFHjx7YunWrKMfc3BwrV66EVCpFx44d0bZtWwwYMABt27aFh4cHWrZsiQMHDohm0d+/fx8ANDbNn47fu3evzDnGxsawsLBAWloaMjNLnkWXl5cHuVwuehAREdHLk0gkCOhniae3tMsvEPD7tjTtFUVEREREFZ57Y0M42uuKYuv2Z4iuuxERvayICzmYvCgRWQrxf1tq2ulg8SQ71KmuV0ImERFRxaSj7QJKUqVKFfzzzz/YtWsXli5dirCwMFHT/Vl2dnYYPnw4AgICtL5PuyYJCQlYvXq1KDZmzBj8/PPPkErV73kICAiAra0tRowYgcOHD6vi1apVg6enJ6ysrNRy/Pz8sG/fPvTr1w+RkZGquJmZGbp06YL33ntP9PysrCwAgJGR5iV5jI2NAUDUNH9eTnFeeno6MjMzYWqqeV+dGTNmYNq0aSUeg4iIiF6c43t66NneBDuOZKliERcUOH8jF64NOEOAiIiIiNRJpRIM6maGX/5KUcVuxxXg9LVcfNDIUIuVEdHbYmdEJhaGpOHZy/vOtfUw/QtbmJvItFMYERHRS6iwzfZiPXv2RM+ePZGdnY2zZ8/i9u3bSE9PR15eHszNzWFrawtXV1c4OTm9ltf39fXF9evXy5WzZs0atSXs27ZtC0EQUFRUhPv372P79u2YNm0a9u3bh4MHD8LBwUH1XEEQMH78eCxYsAD+/v4IDAyEvb09rl69iokTJ2L06NG4fv06lixZInqNoKAgTJ48GT4+Ppg6dSocHR0RExODH374AT/88ANOnTqFf/7554U/i1fp66+/RmBgoOpnuVyOGjVqaLEiIiKit8vwHhY4dDYH8mylKrZ4cxpWfFMVOjJJKZlERERE9K7q4GqE4H8y8CDxyRZE6/fL4eZsAImE55BE9GIEQcBfuzOwfr/66qatmxriuxHWMNCrkIvwEhERPVeFb7YXMzY2RocOHdChQ4c3+rp3795FdHR0uXJycnJKHJPJZKhduzYCAwPh4OCA3r17Y/To0di9e7fqOatXr8aCBQvQq1cvLFu2TBV3c3PDnj170KBBAyxbtgxffvklGjVqBAAIDw/HxIkT4erqis2bN6tmyzdp0gRbtmxBixYtsGfPHuzbtw8fffQRAMDExKTUerOzswFANDv9eTkl5T1LX18f+vr6JY4TERHRyzE1kuJ/vSwwd0OqKhb7qAA7j2SidyezUjKJiIiI6F0lk0owsIsZZq97cg555U4eom7lwYV7KBPRCygqEjB3Qyr2nchWG/NuY4xxA6wg4w3hRERUifF2see4ePGiat/4sj48PDzKdGxfX1+YmJhg//79yM/PV8XXrl0LAOjTp49ajqmpKbp16wZBEHDs2DG1HF9fX7Vl6WUyGfz8/AAAERERqnjNmjUBAA8ePNBYX3G8Vq1aZc7Jzs5Geno6LC0tS222ExER0ev3UWtjONUQ77sZvCcDaZlFWqqIiIiIiCq6zm7GqGIpXsp5nYbZqEREz6PIU+L75UkaG+3DvM0ROIiNdiIiqvzYbNciiUQCKysrFBYWIi0tTRUvbmSbm5trzCuOv2yOi4sLAOD8+fMac4rjTZs2VcXq168PfX19JCUl4eHDh2XKISIiIu2QSSUY089KFMtWCFi5K107BRERERFRhaerI8GALuKVkM7dyMWN2DwtVURElVFGVhEmLkjEySu5orhUAgQOssIwb3NuT0FERG8FNtu1KCYmBnFxcTAzM4ONjY0qXrVqVQDA2bNnNeYVx5/e5/15OWfOnFHLadOmDczNzXHnzh1cvHhRLWfLli0AgB49eqhihoaG6NSpEwBg8+bNZcohIiIi7WlcRx+d3YxEsX3HsxF9jxdLiYiIiEizj9yNYWkmvmyoaa9lIiJN4lMKMXpOAq7H5ovieroSTPvMBt3bmmipMiIioldPK3u2r1mz5o291tChQ9/Ya2myaNEi9O3bV9UMLxYdHY1hw4ZBEAQMHToUMtmT5bl8fHwQERGBuXPnwtvbG25ubqqxxYsX4+jRozA1NUWXLl1EOWvWrMH69evRt29fdO/eXTW2c+dObNiwAVKpFL6+vqq4np4eAgICMH36dIwaNQoHDx6EsbExAGDu3LmIiopChw4d8P7774tqDwwMxL59+/DLL7/A29sbdevWBQCcOHECy5cvh4WFBT799NNX8OkRERHRq/CZjwWOXVIgN08AAAgCsOjvNCycYAeplDMJiIiIiEhMX0+Kvh+aYcX2dFUsMkqBmIf5cHxPT3uFEVGFdzsuH18tSUSqXCmKmxpJMf0LWzSuo6+lyoiIiF4PiSAIwpt+UalU+kaWiJFIJCgsLHztr1MaBwcHxMXFwcXFBU5OThAEAffu3cO5c+egVCrRvn177NmzByYmT+7my83NhaenJ44dOwapVAp3d3fY29vj6tWruHbtGmQyGVatWoUhQ4aocgRBQP/+/VWzzVu0aIHatWvj7t27qtnu06dPxzfffCOqLzc3Fx4eHjh16hSqVauGdu3a4d69ezh16hRsbW1x8uRJODo6qr2vcePGYcGCBTAyMoKnpyfy8/MRGhoKQRCwZcsW+Pj4lOtzksvlMDc3R0ZGBszMzJ6fQEREROWy8aAcf+xIF8W+GmqFLq3evRkFPO+oPPhdERERaU9OrhIDv/sPmTlPGmadWhjhuxE2pWRVTjznqDz4XVVs56Nz8cPyJOTkilsOVSxlmBlQBbWq6WqpMiIiovIr63mH1prtb4JEIkFRUdEbea2SrF+/Hnv37sXZs2cRHx8PhUIBKysrNGvWDAMHDsSQIUM0fh75+flYvHgxQkJCcP36dSgUCtja2qJNmzaYMGECWrVqpZYjCAJWrVqF1atXIyoqCllZWbCwsICbmxtGjx6Nbt26aaxRoVBgxowZ2LBhA+Li4mBlZYVu3brh559/RvXq1Ut8b8HBwVi8eDGuX78OPT09tGrVCt9//z1at25d7s+JJ8pERESvV36BgE9/eYSHSU9uRLQyk2L1j/YwNny3dhbieUflwe+KiIhIu1bvycDqPRmqn6USIPjHaqhe5e1qmPGco/Lgd1VxHT6bjRmrU1D4zOX42va6+C3AFrYWWllkl4iI6IVV6Ga7h4fHG5nZDgCHDx9+I69DL4cnykRERK/fycsKfLMsSRTr19kU/n6WWqpIO3jeUXnwuyIiItIueXYRBn73HxR5Ty4ffuRujElDrLVY1atXEc45oqKisGLFCpw9exb3799HSkoKDAwM4OzsjEGDBsHf3x+6uuKbHKZOnYpp06Y999hHjhxB+/bt1eJJSUmYOXMmdu/ejfv378PQ0BAODg748MMPMXv2bNFzHRwccO/evVJfp3bt2oiJiVH9HBsbi9q1a5f4fDs7O8THxz+3/qdVhO+K1G05JMfSLelq8aZO+vjF3xYmRu/WDd5ERPR2KOt5h1ZuJwsPD9fGyxIRERG901o1MUSrxgY4eSVXFdt6KBNerU1Qs+rbNTuJiIiIiF6embEMvdqbYFNopip28FQ2hnqbw86Ks1RfpYiICCxZsgS1atWCs7MzbG1tkZSUhMjISJw8eRJbt27FwYMHoaenp8pp1qwZhg0bpvF4Dx8+RFhYGIyMjODq6qo2fu7cOXTt2hUpKSlo1KgRevXqBblcjmvXrmHevHlqzfY+ffogOTlZ42sdOXIEsbGxaNeuncZxOzs7jStumpubl/h5UOWgVAr4Y0c6QsIy1cbaNzfEN5/YQE/3zUy6IyIi0haeFRMRERG9Q77sY4mz1x+plvYrUgJLtqTht1G2b2zlISIiIiKqPPp8aIZt4VnIL3g8u71ICYSEyjGmv5WWK3u7eHl5wcvLC46OjqJ4QkICOnfujCNHjmDFihUICAhQjfn4+MDHx0fj8aZMmYKwsDD4+vrCxMRENJaUlIRu3bpBoVBg586d6Nmzp2j89OnTasebM2eOxtdRKpWqbSiHDBmi8TkNGjRAcHCwxjGqvAoKBcxel4Kw0zlqY706mCCgryVkUv6NSUREbz+u30JERET0DqleRRd9PhQve3TmWi5OXFZoqSIiIiIiqsiszGTwam0siu09no1UeVEJGfQiHB0d1RrtwONZ4VOmTAEAHDp0qEzHEgQBGzduBKC5Af7jjz8iOTkZs2fPVmu0A4Cbm1uZ6/7333/x6NEjvPfee+jUqVOZ86hyy8lV4ttlSRob7f/raY4x/dhoJyKidweb7URERETvmI+7mcHaXCaKLdmSrpqtRERERET0tP6eZpA9dRUxv0DAln/l2ivoHVO8V/vTS8iXJjw8HHFxcahatSo6d+4sGlMoFFi3bh2MjY0xfPjwl65t3bp1AIBBgwZBKuWl5ndBWmYRAucn4uz1XFFcKgUmD7HCoG7mXDWNiIjeKVxGnoiIiOgdY2Qgxee+Fvg1OEUVe5RciM3/yjG4G/dNJCIiIiIxOysddG1ljL3Hs1WxnRFZGNDFDGbGslIy6WWlpaUhKCgIAODt7V2mnOIG+MCBAyGTib+fs2fPIjMzE23btoWhoSH27duH0NBQ5Obmol69eujXrx/s7e3L9DoKhQLbt28HAHz88cclPi8hIQE//vgjHj16BHNzc3zwwQfo2bNnmW8eoIojPqUQkxYm4mFSoShuoCfBjyNt8EEjQy1VRkREpD1sthMRERG9gz5saYSdEZm4GpOviq3fL0eXD4xha8lTRCIiIiISG9jFDPtPZEP5/4shKfIEbA/PwjBv3qz5Kt26dQtjNQhZAABQvklEQVTTp0+HUqlEQkICjh8/jqysLPj7+2Pw4MHPzc/NzcXWrVsBaF5C/tq1awCAKlWqwMfHBzt37hSNf/PNN1i5ciUGDhz43NfasWMHMjMz0bRpUzRt2rTE5924cQM//fSTKFazZk1s3rz5uUvW5+XlIS8vT/WzXM4VFbQl5mE+pixOQkqGeAsJcxMpfv3SFg0d9LVUGRERkXZxbR8iIiKid5BEIsHoflZ4enW/3HwBy7ena60mIiIiIqq43quiC4/3jUSxbYczkZOr1FJFb6eEhASsXr0aa9euxcGDB5GVlYUxY8Zg5syZZVqmfdeuXcjIyECjRo3QvHlztfG0tDTV8/bv348lS5YgMTERsbGxmDhxIhQKBYYNG4aLFy8+97XWrl0LQHNTHwD09fXxxRdfIDw8HAkJCZDL5Thx4gS8vLxw//59dO3aFffu3Sv1NWbMmAFzc3PVo0aNGs+ti169K3fyMG5uglqjvaq1DAsn2LHRTkRE7zQ224mIiIjeUfVq6sG7jYkoduhsDi7dyi0hg4iIiIjeZYO7mYl+zsxRYtfRLC1VU7H4+vqiQYMG5XqcPn1a7Tht27aFIAgoLCxETEwMgoKCEBwcjBYtWiA2Nva5dRQvIV9SA1ypfHxzRGFhIX766Sd8+eWXsLW1Ra1atTB79mz07dsXBQUFmD17dqmvk5iYiNDQUEilUgwaNEjjc6pVq4alS5eiQ4cOqFKlCkxNTdGqVSvs2bMHgwYNQnp6On799ddSX+frr79GRkaG6hEXF/e8j4BesZOXFZi0MBFZCkEUr22vi4UT7FDDTldLlREREVUMXCOUiIiI6B02ooc5ws9liy6cLPo7Dcu/qgqZTFJKJhERERG9a2rb66FNU0NERilUsc3/yuHbwQT6eu/2nJ67d+8iOjq6XDk5OTkljslkMtSuXRuBgYFwcHBA7969MXr0aOzevbvEnJSUFOzfvx9SqbTEJedNTJ7cbDt8+HC18eHDh2Pz5s04cuRIqbVv2rQJhYWF8PT0LPMe70/75ptvsGHDBhw4cKDU5+nr60Nfn7OmteXgqWzMWpsC5TMLWDSuo4/pX9jC1Ojd/v97IiIigDPbiYiIiN5pFqYyDO9hIYrFPCzAP5GcofQ6RUVFISAgAK1atYK9vT309fVhbm4Od3d3LFq0CAUFBSXm/v333+jUqRMsLS2hq6sLOzs79OrVC+Hh4SXmhIaGwtvbG7a2ttDV1YW1tTW6dOmC7du3l5ijUCjwww8/oF69ejAwMIC9vT1GjBiBhw8flvregoOD4ebmBhMTE1hZWcHLywvHjx9/7mdCRERElcOzs9vT5ErsPZ6tpWoqjosXL0IQhHI9PDw8ynRsX19fmJiYYP/+/cjPzy/xeSEhISgoKECHDh1QvXp1jc+pVasWAMDIyAi2trZq4w4ODgAez1wvTfEM+o8//rgsb0FN3bp1AQCPHj16oXx6/Tb/K8dvq9Ub7a0aG2DWaDbaiYiIivE3IhEREdE7rmc7E9S2Fy/9t2p3BjKyikrIoJcVERGBJUuWID4+Hs7OzvDz80PLli1x8eJFjBkzBp6enhovpI4fPx79+/fH0aNH0bx5c/j5+aF69erYtWsXOnbsiBUrVqjlzJ8/H126dMG+fftQr1499O7dGw0aNEBYWBj8/Pzw7bffquXk5uaiU6dO+Pnnn5GVlYVevXqhRo0aWLVqFZo3b46YmBiN72vcuHEYPnw4rly5gs6dO8PNzQ2hoaFo3749duzY8dKfGxEREWlfAwd9vN/AQBQLCZWjoFAoIYNelkQigZWVFQoLC1V7rmvyvCXkAaj2cVcoFMjLy1MbT01NBSCeAf+smzdv4syZMzAyMoKfn1+Z3sOzit+HsbHxC+XT6yMIAv7YkY5lW9PVxrp8YIyfPreFwTu+kgUREdHT+FuRiIiI6B0nk0kQ0NdSFJNnK7Fqd4aWKnr7eXl54c6dO4iNjUVYWBg2btyIsLAwxMbGonHjxjhy5Iha4zwqKgrz58+HhYUFLl26hEOHDiEkJATnzp3Dxo0bIZFIEBgYiKysJ6sSJCUl4auvvoKuri4OHz6MyMhIbNq0CZGRkQgPD4e+vj5mzJih1jz/5ZdfcPLkSbi7u+PmzZsICQnBqVOnEBQUhKSkJIwYMULtPYWFhWHBggWwtrbGpUuXsGPHDuzfvx8RERGQyWQYPnw40tPTX8vnSURERG/Wx8/Mbk9MK0LYac5uf11iYmIQFxcHMzMz2NjYlPicEydOwNDQEL179y7xWDVr1oSLiwsEQdC4VHxxrLgpr0lxU794xv2L2Lp1KwDA1dX1hfLp9SgqEhC0PhUbD8rVxvp+aIrJQ6ygw+3GiIiIRNhsJyIiIiI0r2+ADq5Gotg/x7JwO67kZSrpxTk6OsLR0VEtbmdnhylTpgAADh06JBqLiIgAAPTv3x/Ozs6isQEDBqBJkybIzs7GtWvXVPFTp04hLy8PnTp1QocOHUQ57du3R9euXSEIAs6ePauK5+fnY/HixQCAJUuWiC6gBgYGomnTpjhy5AjOnTsnOt7cuXMBAN99951qWVAAcHd3h7+/P9LT07Fy5crnfDJERERUGTStq4/GdcT7aG88KIcgcHb7i1q0aBHi4+PV4tHR0Rg0aBAEQcDQoUMhk8k05hc3wHv16gUzMzONzyk2efJkAMDEiRNFy7hfvHgRQUFBAAB/f/8S89evXw+g9Bn0APDHH3/gxo0bavFt27bhq6++AgCMGjWq1GPQm5NfIGDqn8kat4X4zMcC/n4WkErZaCciInoWm+1EREREBADw97OAvu6TiydKAVi0OY0XTd8wXd3HS/rr6emJ4vr6+pqersba2vqlciIjI5GRkYE6deponNHUp08fAMDu3btVMYVCobo5oHj8eTlERERUeUkkErW92x8kFuLCTfVlyalsgoKC8N5778HV1RX9+vVD37594ebmBmdnZ5w6dQrt27fHjBkzSswvawMcAAYNGoRhw4bh8uXLcHZ2hre3Nzp16oRWrVohNTUVI0eORN++fTXmHj9+HDExMahatSo6d+5c6uusX78eDRs2hIuLC/r27YvevXujYcOG6N27NzIzMzFp0iT4+vo+t156/bIUSkxZnIjISwpRXCoBJn1shQFdzCCRsNFORESkCZvtRERERAQAsLPSwaCu4ouml2/n4dDZHC1V9O5JS0tTzSby9vYWjXXs2BE6OjoICQkRzV4HgE2bNuHy5cvo0KED6tSpo4q7ubnBwsIChw4dUlsmNCIiAgcOHEDdunXRrl07VfzSpUsASl7SszgeFRWlikVHRyMvLw+2traoXr16mXKIiIiocnNzNkCtarqi2O6jWSU8m55n+vTpGDBgALKzs3HgwAHs3LkT9+/fh6enJ4KDg3H48OESl2w/ffo0bt68iSpVqqBLly5ler1Vq1ZhxYoVqFOnDsLDw3H69Gm4uroiODhYbTujpxXPoB84cGCJs+yLjRw5En369EFOTg4OHjyI3bt3Qy6Xw8/PD6GhoZg1a1aZaqXXK1VehMB5Cbh0S3yzjK4OMHWkDT5q/WJbBRAREb0rdLRdABERERFVHP06m2LfiSzEpxSpYsu3p6N1E0MYGvA+zVft1q1bmD59OpRKJRISEnD8+HFkZWXB398fgwcPFj3XyckJ8+bNw9ixY+Hi4oJ27dqhSpUquHXrFi5cuIAePXrgr7/+EuWYm5tj5cqVGDRoEDp27IjWrVujevXqePDgAY4fP442bdpgzZo1oln09+/fBwCNTfOn4/fu3StzjrGxMSwsLJCWlobMzEyYmppqfF5eXh7y8p5c5JPL1feKJCIioopBIpGgR1sTLN6cpoodu5iD1IwiWJmX3oQldYMHD1Y7/ysrNze3cq9GJZFIMHLkSIwcObJceUuXLsXSpUvL9NyXeU/0ZvyXXIjJixLxX1KhKG5sIMEv/rZwqWegpcqIiIgqDzbbiYiIiEhFX0+KL3pb4scVyapYcnoRNhyQ49NeFtor7C2VkJCA1atXi2JjxozBzz//DKlU/eaGgIAA2NraYsSIETh8+LAqXq1aNXh6esLKykotx8/PD/v27UO/fv0QGRmpipuZmaFLly547733RM/Pyno8I83IyEhjzcbGxgCAzMzMMucU56Wnp5fabJ8xYwamTZtW4jGIiIioYvH8wBh/7EhHXsHjRm+REth/IguDuplruTIiep47D/IxZXEiUuVKUdzSTIqZo6rAqYZeCZlERET0NE5PIiIiIiKRti6GeL+BeAbD3//K8TCpQEsVVTy+vr5o0KBBuR6nT59WO07btm0hCAIKCwsRExODoKAgBAcHo0WLFoiNjRU9VxAEjBs3DgMGDMDQoUNx8+ZNZGVl4dSpU3BycsLo0aMREBCg9hpBQUHo3Lkz2rdvj6ioKGRlZSEqKgqdOnXCDz/8AD8/v9f1MZXb119/jYyMDNUjLi5O2yURERFRKUyNpOjYQnyz3T+RWShSlm+WNRG9WVG3czFuXoJao72atQwLJ9ix0U5ERFQOnNlORERERCISiQQBfS3xv+mPUPT/114KCoGlW9Ix/Qtb7RZXQdy9exfR0dHlysnJySlxTCaToXbt2ggMDISDgwN69+6N0aNHY/fu3arnrF69GgsWLECvXr2wbNkyVdzNzQ179uxBgwYNsGzZMnz55Zdo1KgRACA8PBwTJ06Eq6srNm/erJot36RJE2zZsgUtWrTAnj17sG/fPnz00UcAoNoLtKR6s7OzAUA0O/15OSXlPUtfXx/6+voljhMREVHF06OtCfafyFb9HJ9ShLPXc/FBI0MtVkVEJTkelYOfVqYgv0B8U4zje7qYGVAF1twGgoiIqFw4s52IiIiI1NSqpgtfD3FT9MRlBU5dVWipoorl4sWLEAShXA8PD48yHdvX1xcmJibYv38/8vPzVfG1a9cCAPr06aOWY2pqim7dukEQBBw7dkwtx9fXV21ZeplMpprVHhERoYrXrFkTAPDgwQON9RXHa9WqVeac7OxspKenw9LSstRmOxEREVU+DRz04FRdVxT752iWlqohotIcOJmFH1YkqzXamzjpY/54OzbaiYiIXgCb7URERESk0VBvc1iaik8Xl2xOQ0EhlwV9nSQSCaysrFBYWIi0tDRVvLiRbW6ueQ/U4vjL5ri4uAAAzp8/rzGnON60aVNVrH79+tDX10dSUhIePnxYphwiIiJ6O0gkEvRoZyKKnbisQFJaoZYqIiJNQkLlmLkmFUrxyvFwb2KIWQG2MDFiq4CIiOhF8DcoEREREWlkYijF/3pZiGIPEgux9XCmdgp6R8TExCAuLg5mZmawsbFRxatWrQoAOHv2rMa84riDg0OZc86cOaOW06ZNG5ibm+POnTu4ePGiWs6WLVsAAD169FDFDA0N0alTJwDA5s2by5RDREREb48PWxrDUF+i+lkpAHuPZ5eSQURviiAIWLE9Dcu3p6uNdW1ljJ8+s4G+HtsEREREL4q/RYmIiIioRF1bGaN+LT1RbO3eDKRkFGmporfDokWLEB8frxaPjo7GoEGDIAgChg4dCpnsyTKOPj4+AIC5c+fi9OnTorzFixfj6NGjMDU1RZcuXdRy1q9fj3/++UeUs3PnTmzYsAFSqRS+vr6quJ6eHgICAgAAo0aNUu21XvzaUVFR6NChA95//33R8QIDAwEAv/zyC27duqWKnzhxAsuXL4eFhQU+/fTT5342REREVPkYGUjRuaWxKLYnMgtFRVwRiUibiooEzF6Xik2h6jdM9+tsislDrCCTSTRkEhERUVlJBEHgWS9pnVwuh7m5OTIyMmBmZqbtcoiIiOgp1+/mYdTsBFHMr6MpAvpaaqmil1MRzjscHBwQFxcHFxcXODk5QRAE3Lt3D+fOnYNSqUT79u2xZ88emJg8WZI1NzcXnp6eOHbsGKRSKdzd3WFvb4+rV6/i2rVrkMlkWLVqFYYMGaLKEQQB/fv3V802b9GiBWrXro27d++qZrtPnz4d33zzjai+3NxceHh44NSpU6hWrRratWuHe/fu4dSpU7C1tcXJkyfh6Oio9r7GjRuHBQsWwMjICJ6ensjPz0doaCgEQcCWLVtUzf+yqgjfFREREZXNrbh8fD5DfDPhz5/boI2LkZYqKjuec1Qe/K7KLi9fiZ//SsHxKIXa2Ge+Fhjgyc+PiIioNGU97+DMdiIiIiIqVcPa+ujaSjxT6Z9jWUjl7PYXNn36dAwYMADZ2dk4cOAAdu7cifv378PT0xPBwcE4fPiwqNEOAAYGBvj3338RFBSEFi1aICoqCtu3b0daWhr69OmDY8eOiRrtwOM9VENCQrBy5Uq0b98et2/fxvbt2xEbGwsvLy/s27dPrdFe/FqHDx/G999/DyMjI+zYsQP37t3DJ598gvPnz2tstAPA/PnzsWrVKjRs2BChoaE4ceIEOnfujIiIiHI32omIiKhyqVtDDw0cxCsi7T6WpaVqiN5tWQolpixOUmu0S6XApCFWbLQTERG9QpzZThUC70olIiKq2OJTCjHkx/9QpHwS69fZFP5+lW92O887Kg9+V0RERJXLvhNZmL02VfWzRAKsm2aPajY6Wqzq+XjOUXnwu3q+1IwiTFmSiDsPCkRxPV0Jvv/UGm2aVvzVJoiIiCoCzmwnIiIiolemqrUOunwgnt2+KyIL6Zmc3U5EREREj3V83wjGhk/2fxaEx3u3E9Gb8Si5EGPmJqg12o0NJJgZYMtGOxER0WvAZjsRERERlcmgrmaQPrl2itx8AVsOZWqvICIiIiKqUAz0pGo3aO47kYWCQi6sSfS6xT4qwJigBPyXVCiKW5pJMT/QDi51DbRUGRER0duNzXYiIiIiKpP3qujiw5bimRA7jmRCns3Z7URERET0WPe2JqKf0+RKRD6zbzQRvVrR9/Iwbm4CUjLEf5tVs9HBoolVUae6npYqIyIievux2U5EREREZTa4mzkkT81uz8kVsO0wZ7cTERER0WO17fXQpI6+KLb7KM8XiV6XizdzMWFBIuTZSlHc0V4XiybYwd5GR0uVERERvRvYbCciIiKiMqtZVRceruLZ7VsPZyJLoSwhg4iIiIjeNT3aiWe3X4jOQ1xCQQnPJqIXdTwqB1MWJyInV7xVg3NtPcwLtIOVuUxLlREREb072GwnIiIionL5+CMz0c/ZCgE7wjlbiYiIiIgea9/cCGbG4suO/xzL0lI1RG+nsNPZ+GFFMgrEW7Tj/QYGmD26CkyNeOmfiIjoTeBvXCIiIiIql9r2emjXzFAU23IoEzm5nN1ORERERICergRdWxmLYgdOZiO/QCghg4jKY+eRTMxYnQLlM3+CtWtmiOlf2MLQgJf9iYiI3hT+1iUiIiKichvykbnoZ3m2ErsiOFuJiIiIiB7r3la8lLw8W4mICzlaqobo7SAIAtbvz8CCkDQIz9y70rWVMX741AZ6uhLtFEdERPSOYrOdiIiIiMrNqYYe3JuIZ7f/HSZHbj5ntxMRERERUMNOF6719UWx3Ud5cybRixIEAcu3p2Plrgy1sd6dTDHpYyvIZGy0ExERvWlsthMRERHRCxnyzN7t6VlKXkAlIiIiIpXu7UxFP1++k4e7/+VrqRqiyqtIKSBoQyr+DstUG/ukuzm+7G0BqZSNdiIiIm1gs/01i4qKQkBAAFq1agV7e3vo6+vD3Nwc7u7uWLRoEQoKCkrM3bdvHzw9PWFhYQEjIyM0adIEs2bNKjWnqKgI8+bNQ5MmTWBoaAhbW1v069cP169fL7XO3bt3o0OHDjAzM4OZmRk8PDywZ8+eUnOuXr2Kvn37wtbWFoaGhmjSpAnmz58P5bObBREREdFbqYGDPtycDUSxkFA58ji7nYiIiIgAtGlqCEsz8eXHf47x5kyi8igoFPDLXynYG5mtNhbQ1xJDvcwhkbDRTkREpC1str9mERERWLJkCeLj4+Hs7Aw/Pz+0bNkSFy9exJgxY+Dp6Yn8fPU7emfOnAkvLy8cOnQIzs7O6NKlCxITEzFlyhR4eXlpbLgrlUr07dsXgYGBePDgAby9vdGoUSNs2bIFLVq0wOnTpzXWOH/+fPTs2RPHjx9HmzZt0KlTJ5w+fRrdu3fH4sWLNeacOHECLVu2xJYtW+Do6IiePXsiOTkZ48ePx4ABAyA8u2kQERERvZU+fmbv9lS5EnuPq18EIiIiIqJ3j66OBF7u4r3bD57KhiKPN2cSlYUiT4nvfk/CkfM5orhUCnw11Ap+HU1LyCQiIqI3hc3218zLywt37txBbGwswsLCsHHjRoSFhSE2NhaNGzfGkSNHsGLFClHOmTNn8PXXX0NXVxd79+7F8ePHsWPHDty6dQsdO3ZEWFgY5syZo/Zaf/31F7Zv3466devixo0b2LJlC8LDw7F582bk5ORg8ODBKCwsFOVER0dj4sSJ0NfXR0REBPbt24cdO3bg4sWLsLa2xvjx43H79m1RTkFBAQYPHgyFQoG5c+fi1KlTCAkJwa1bt+Du7o7Nmzdj9erVr/7DJCIiogqncR19tb04Nx2UI7+AN94REREREeDd1gRPT7rNVggIP5dTcgIRAQCycpSYsjgJZ67liuK6OsDU/9mgSyuTEjKJiIjoTWKz/TVzdHSEo6OjWtzOzg5TpkwBABw6dEg0tnz5cgiCgE8++QRdu3ZVxc3MzPD7779DIpFg7ty5KCoqEuXNnTsXADBr1izY2dmp4r1790bPnj1x+/Zt7Ny5U5SzYMECFBUVwd/fH+7u7qp4vXr18O2336KwsBALFiwQ5Wzfvh13796Fi4sLxo8fr4qbmJioZsIHBQU9/8MhIiKit8KQZ2a3J6UX4eApzm4nIiIiIqCqtQ5aPrP10G4uJU9UqrTMIgTOT8CVO3miuIG+BDO+rIK2zYy0VBkRERE9i812LdLV1QUA6OnpieLnzp0DAHh4eKjl1KtXD/b29khOTkZkZKQqfvfuXVy/fh2Ghobw9vZWy+vTpw+Ax3uzP614X/bi8ZfNcXV1haOjI65cuYLY2Fi1cSIiInr7uNQzQFMn8ez2DQcyUFjE2e1EREREBPRoJ56BeyM2Hzfvq2+rSERAQmohxgYl4PYD8TaipkZSBI2pAtcGBiVkEhERkTaw2a4laWlpqtnfzzbHs7MfzwSztLTUmGttbQ0AuHTpkipW/O/GjRurmvhPc3V1BQBERUWpYunp6bh//z4AoHnz5mo5NWrUgI2NDe7duwe5XK72WsXHLMtrERER0dttiJd4dnt8ShFCT3N2OxEREREBrRoZwsZCJor9w9ntRGruJxRgbFACHiSKtwK1NpdhfmAVNKytX0ImERERaQub7W/IrVu38Mknn2Do0KHo2rUratasiTNnzsDf3x+DBw8WPdfW1hYAcO/ePbXjCIKgij89Xtw0r169usbXL45ryrG0tISxsXG588rzWs/Ky8uDXC4XPYiIiKjycq2vD+fa4tV61u+Xo4iz24mIiIjeeTKZBN5txLPb/z2TjZxcpZYqIqp4bsXlY1xQAhLTxFuHVrN+3Givba9XQiYRERFpE5vtb0hCQgJWr16NtWvX4uDBg8jKysKYMWMwc+ZMSKXir6F9+/YAgNWrV6sdZ+vWrcjIyAAAZGZmquJZWY/vBjYy0rxfT3EzvTw5L5qnKedZM2bMgLm5uepRo0aNEp9LREREFZ9EIlGb3f5fUiEOnc3RUkVEREREVJF4tTHG05fAFHkCwrgSEhEAIOp2LgLnJSA9S3wDSq1qulgwwQ7v2aqvZEpEREQVA5vtz+Hr64sGDRqU63H69Gm147Rt2xaCIKCwsBAxMTEICgpCcHAwWrRooba3+ZdffglTU1OcPHkSQ4cOxa1bt5Ceno6///4bn3/+OXR0dABArUlfmXz99dfIyMhQPeLi4rRdEhEREb0kN2cD1K/57Oz2DBQpObudiIiI6F1na6ED98aGotjuY1kQBJ4r0rvt1FUFpixKQnau+P8X6tfSw/zxVWBjoaOlyoiIiKgsKm+39g25e/cuoqOjy/XIySl5BpdMJkPt2rURGBiIVatW4datWxg9erToOTVq1MC2bdtgYWGBtWvXol69erC0tET//v1Rp04djBgxAoB4T3cTk8dLcZX02sX7wJuampY550XzNOU8S19fH2ZmZqIHERERVW4SiQQfe4l/p99PKETEec5uJyIiIiKgRzvxUvJ3HhTgRmy+lqqpWKKiohAQEIBWrVrB3t4e+vr6MDc3h7u7OxYtWoSCggK1nKlTp0IikTz3ERERocoJDw8vU85PP/2ksc7g4GC4ubnBxMQEVlZW8PLywvHjx0t9b5GRkfDy8oKVlRVMTEzg5uaGNWvWvNwH9pY4fDYb3y1LQl6BuNHerJ4+gsZWgbmJTEuVERERUVnxtrjnuHjx4ms7tq+vL0xMTLB//37k5+dDT+/JTLDOnTsjJiYGmzZtwpUrVyCTydC6dWv07t0bw4cPBwA0atRI9fyaNWsCAB48eKDxtYrjtWrVUstJS0tDdna2xn3bS8pLS0vDgwcP0LRp0zLlEBER0buhdRND1KmuizsPnlwMXLdfjg6uRpBKJVqsjIiIiIi0rUVDA1S1liE+5cme1LuOZqFhbX0tVlUxREREYMmSJahVqxacnZ1ha2uLpKQkREZG4uTJk9i6dSsOHjwoun7YrFkzDBs2TOPxHj58iLCwMBgZGcHV1VUVr1q1aok5RUVFWLduHQCgXbt2auPjxo3DggULYGhoiC5duiA3NxehoaE4ePAgtmzZAh8fH7WcrVu3on///lAqlWjfvj1sbGzw77//YtiwYYiKisKcOXPK8zG9Vf45loV5G1Px7OIOrZsa4odPbaCny7+fiIiIKgM227VIIpHAysoK9+/fR1paGuzs7ETjlpaW+OKLL9TyTpw4AalUqtrbHQBcXFwAAFeuXEFBQQF0dcX7+Jw/fx4ARM1xCwsL1KxZE/fv38eFCxfQtm1bUU5cXBySk5NRq1Yt0cxzFxcXXLp0CefPn4eXl5dafZpei4iIiN4NEokEQz4yx9Q/klWxu/8V4NglBdo3N9JiZURERESkbVKpBN3bmODPXRmqWPi5HHzZxxKmRu/2ApxeXl7w8vKCo6OjKJ6QkIDOnTvjyJEjWLFiBQICAlRjPj4+GhvcADBlyhSEhYWpJvsUa9CgAYKDgzXm7Nu3D+vWrUONGjXg4eEhGgsLC8OCBQtgbW2NEydOoG7dugAeX6f08PDA8OHD4eHhAQsLC1VOamoqRowYgaKiImzduhV+fn6q99S2bVsEBQWhe/fuaq/1LtgUKseK7elq8c5uRpg8xBo6MjbaiYiIKot3+yxWy2JiYhAXFwczMzPY2NiUKWfPnj2IiYlBt27dUKNGDVW8du3aaNiwIRQKBfbs2aOWt2XLFgBAjx49RHFvb2/R+MvmXLhwATExMWjcuDEcHBzK9J6IiIjo7dLWxRAO1cQ3/q3bn8H9OImIiIgI3VqbQPbUFcm8AgGhp7K1V1AF4ejoqNZoBwA7OztMmTIFAHDo0KEyHUsQBGzcuBEAMGTIkDLXUDyrffDgwZBIxM3euXPnAgC+++47VaMdANzd3eHv74/09HSsXLlSlPPnn39CLpejV69eqkZ78XuaNWsWACAoKKjM9b0NBEHAnzvTNTbae3UwwVdD2WgnIiKqbNhsf80WLVqE+Ph4tXh0dDQGDRoEQRAwdOhQyGTi/XfOnTundkH6+PHjGD58OAwMDFQnuE8LDAwEAEyePBmJiYmq+LZt27Br1y44OTmhV69eopyxY8dCJpPh999/x8mTJ1XxW7duYfr06dDR0cHYsWNFOb6+vqhduzYuXbqEefPmqeLZ2dkYNWoUAGDChAmlfi5ERET09pJKJfj4I/He7bfjCnDySq6WKiIiIiKiisLKTIa2zcQrHu0+mskbM0tRvILl00vIlyY8PBxxcXGoWrUqOnfuXKac7Oxs7Ny5E4B6g16hUKga/X369FHLLY7t3r1bFC+eEKQpx9vbGwYGBggLC0Nu7rvxd4JSKWDh32nYcECuNvbxR2YY08+SW28RERFVQlxG/jULCgrCuHHj4OLiAicnJwiCgHv37uHcuXOqvYpmzJihlte7d28UFRWhcePGsLS0xK1bt3Du3DkYGBhgy5YtqF+/vlrOiBEjsHfvXmzfvh0NGjTAhx9+iOTkZBw5cgSGhoZYt24ddHTEX3n9+vUxe/ZsBAYGol27dvD09ISenh4OHjwIhUKBhQsXwsnJSZSjq6uLdevWoXPnzggMDERISAhq1aqFo0eP4tGjR+jTp0+Jez8RERHRu6GDqxFW78lAXEKhKrZmbwZaNTZQmyVDRERERO+WHu1McOR8jurne/GFuHwnD02dDLRYVcWUlpammv1dvNrk8xTPUB84cKDaBJ+SbNu2DdnZ2WjevDmcnZ1FY9HR0cjLy4OtrS2qV6+ullu8J3xUVJQofunSJdH40/T09NC4cWOcPXsWN2/efOu3oywqEjBnfSoOnFRfxeGL3hb/196dx0dV3f8ff09CMiF7gLDvIKhsQpEdZAtUEJBVBIpatUULRLBfBW21G0XbBkRbv2gVoVT8IQgKsoqCUEDZpShi2EKQHZJAQoAs5/uHv4xOJtvIZObO5PV8PObxmDl3zp0zOQ737Xzm3KuRfaKL6AUAAPwBK9vL2YwZMzR69GhlZWVp3bp1+uCDD3TixAklJCRo/vz52rhxo9N1kwpMmDBBderU0Y4dO/Tee+/p/PnzevTRR3XgwIFig3VQUJCWLFmipKQk1a5dWx9++KH++9//avjw4dq1a5c6duxYZL8pU6ZoxYoV6ty5s7Zs2aKPP/5Y7du318qVKzVp0qQi+3Tp0kU7d+7U8OHDdfjwYa1YsUJVqlTRrFmztHjxYr5EBwCgggsOsmlsf+cvjA6l3NCugxVj1QoAAACK17aZXXWrOy8IWbkl00ejsZbk5GQ9+OCDGj9+vPr376/69etr586dmjBhgsaOHVtq/2vXrum9996T9ONOIV9UnxMnTkhSkYV2SYqIiFBsbKzS0tJ05coVSdLly5eVkZFRYr+C9pSUlGLHdf36dV2+fNnp5m9yco3+9NZFl0J7kE36n3FVKLQDAODnWNlezsaOHVumIFzYtGnTNG3aNLf7BQcHa+rUqY5TypfVoEGDXK7NXpoWLVoUed12AAAASepzZ4QWrL6s0xecV7e3v43V7QAAABWZzWbTPd0iNXdZuqNt896rmjgyTzGRZVuJHajOnj2rBQsWOLVNnjxZf/zjHxUUVPq6qRUrVigjI0MtWrRQ27Zty/Sap0+f1scff6zg4GDdf//9LtszM7/7IUR4eLjLtgIRERFKT0/XlStXFBUV5ehTUr+IiAhJchToizJz5kz9/ve/L9P7sKIbOUa/++d5l0tqBQdJz/68mnq2K/5vCgAA/AMr2wEAAFAugoNdV7d/efSG9n5z3UcjAgAAgFX07xShkB8sA8rJldZudz3Ftr8YOnSobr31VrduO3bscNlPt27dZIxRbm6ujh49qqSkJM2fP1/t27fX8ePHSx1HSSvUi/POO+8oLy9PCQkJqlmzZpn7ecP06dOVkZHhuKWmpvp6SGWWfS1f018951JoD6kk/fGX8RTaAQAIEKxsBwAAQLlJ6BihhWsydPZSnqNt4eoMtWvO9TgBAAAqspjIYN3VLlwbdnx/7fYP/5OpkX2iFBTkf2dBOnbsmA4dOuRWn6tXrxa7LTg4WI0aNdLUqVPVsGFDDR8+XJMmTdLKlSuL7XPx4kWtXbtWQUFBbp1ps7QCfcElMEsab1bWdz+UiIqKcupT0C862vVU6YX7FMVut8tut5c0fEvKvPpdof3Lozec2sPsNv1pQjz/PwQAQABhZTsAAADKTUglm+7v5/zF2hfJ17X/MNduBwAAqOgGdYt0evzt+Vy/PQvSvn37ZIxx69azZ88y7Xvo0KGKjIzU2rVrdePGjWKft3jxYuXk5Oiuu+4q9jrphR08eFB79+5VZGSk7r333iKfU79+fUnSyZMni9yelZWl9PR0xcXFOQrn0dHRiomJKbFfQXuDBg3KNFZ/kZGZp6lzzroU2iMq2/SXidUptAMAEGAotgMAAKBc/bRzpKrFOl97c+Hqyz4aDQAAAKyiZRO7GtYKcWpbuaX463dXVDabTVWqVFFubq7S0tKKfd6POYX8woULJUnDhg0r9trqzZs3l91u1/nz5/Xtt9+6bN+zZ48kqXXr1k7tbdq0cdr+Qzk5OTpw4IDCwsLUrFmzMo/X6i6k5+qJ2ed0ODXHqT06IkhJiTXUson/rdIHAAAlo9gOAACAchUaYtPoBOfV7bu/vqavjvnnqiUAAAB4hs1m06Duzqvbt36RrYsZecX0qJiOHj2q1NRURUdHq1q1asU+Z/v27apcubKGDx9epv0aY7Ro0SJJJRfoK1eurN69e0uSlixZ4rJ96dKlkqRBgwY5tQ8cONBp+w99+OGHunbtmvr27auwsMBY6X3m4neF9pTTzoX2qjHBmj2luprVD/XRyAAAQHmi2A4AAIByN7BrhOKinaPnwtUZPhoNAAAArCKhQ4TsId9foz0vX1qzLdOHI/KNV155RWfOnHFpP3TokMaMGSNjjMaPH6/g4OAien+/qn3IkCFFXh+9KFu2bFFKSorq1KnjKKYXZ+rUqZKkP/3pT0pOTna0b9++Xa+99ppiY2P18MMPO/V55JFHFB0drQ8++EDLli1ztJ87d05PPfWUJOnJJ58s01itLvVsjp6YdVanzuc6tVevEqyXplRXo9oU2gEACFQU2wEAAFDu7KFBLqvbP//ymg6lsLodAACgIosMD1Lv9s6nL1+1NVN5+cZHI/KNpKQk1alTR+3atdOoUaM0cuRIdejQQbfffrs+//xz9ejRQzNnziy2/9tvvy3JvVPIFxTox4wZo6Cgkr8m7tu3rxITE3Xx4kXdcccduvfeezVgwAD16NFDubm5euuttxQbG+vUp0qVKpo3b56CgoI0YsQI9e7dWyNHjlTz5s11+PBhTZ06tczXrbeyY6du6InZZ3UuzfmMDHWrV9KcqTVUp3pIMT0BAEAgoNgOAAAAr7inW6RiIp3j57/XcO12AACAiq7wqeTPXsrTzq+u+Wg0vjFjxgyNHj1aWVlZWrdunT744AOdOHFCCQkJmj9/vjZu3KjIyMgi++7YsUPffPONqlevrn79+pXp9a5fv+44vfu4cePK1Oell17SW2+9pdtuu00fffSRtm/frr59+2rz5s269957i+wzfPhwbd68Wf3799fevXu1evVqNW3aVPPnz1dSUlKZXtfKDqVc15TZ55R2Od+pvVHtEL00pYZqVKnko5EBAABvsRljKtbPRGFJly9fVkxMjDIyMsp8qisAAOB/3ll/Wf98P92p7Z/P1FSTut47rSK5w38wVwAAVAzGGP3yhTM6nPr9ta47t6qsGY/Fe+X1yRz+w0pz9d/D1zT91fO6es356/Vm9UP14sR4xUQWfcp/AADgH8qaO1jZDgAAAK8Z0iNS0RGFrt2+hmu3AwAAVGQ2m02Du0c5tX1+IFvnLuUW0wPwrV0Hs/XUK66F9lZN7PpbYnUK7QAAVCAU2wEAAOA14WFBGt7b+YvULfuydezUDR+NyPv279+viRMnqlOnTqpdu7bsdrtiYmLUuXNnvfLKK8rJySm275o1a5SQkKDY2FiFh4erVatW+stf/lJin7y8PM2ePVutWrVS5cqVFR8fr1GjRungwYMljnPlypW66667FB0drejoaPXs2VOrVq0qsc+XX36pkSNHKj4+XpUrV1arVq300ksvKT8/v8R+AAAAvduHKzzM5nicb6TV2zJ9OCKgaFv3X9Wz/3te13OcC+0/uTVML0yMV2RlvnIHAKAi4cgPAAAArxraM0oRlb//ItUY6e21Fefa7Zs3b9Y//vEPnTlzRrfffruGDRumO++8U/v27dPkyZOVkJCgGzdcf3zw4osvasCAAfrkk090++23q1+/fjp37pyefvppDRgwoMiCe35+vkaOHKmpU6fq5MmTGjhwoFq0aKGlS5eqffv22rFjR5FjfOmllzR48GBt27ZNXbt2Ve/evbVjxw7dc889+vvf/15kn+3bt+vOO+/U0qVL1bhxYw0ePFgXLlzQlClTNHr0aHH1KgAAUJLwsCD1uTPCqW3V1izl5ZEhYB2f7MrS869fUE6hky50af3dZQ8q2/m6HQCAioajPwAAALwqsnKQhvV0Xt2+afdVpZ4tfnV2IBkwYICOHDmi48ePa8OGDXrnnXe0YcMGHT9+XC1bttSnn36q119/3anPzp07NX36dIWEhGj16tXatm2b3n//fSUnJ6tXr17asGGD/va3v7m81rx587R8+XLdcsst+vrrr7V06VJt2rRJS5Ys0dWrVzV27Fjl5jp/U3jo0CH9+te/lt1u1+bNm7VmzRq9//772rdvn6pWraopU6bo8OHDTn1ycnI0duxYZWdna9asWfr888+1ePFiJScnq3PnzlqyZIkWLFjg+T8mAAAIKIO7Rzo9vpiRp+3/zfbRaABna7ZlasZbF1X4pE292ofrd49WU2iIreiOAAAgoFFsBwAAgNcN7x2lynbn04QuWlcxVrc3btxYjRs3dmmvUaOGnn76aUnSJ5984rTttddekzFGDz74oPr37+9oj46O1ty5c2Wz2TRr1izl5eU59Zs1a5Yk6S9/+Ytq1KjhaB8+fLgGDx6sw4cP64MPPnDqM2fOHOXl5WnChAnq3Lmzo71Zs2Z69tlnlZubqzlz5jj1Wb58uY4dO6Y2bdpoypQpjvbIyEjHSvikpKTS/zgAAKBCa1I3VLc1DHVqW/kfTiUP31u28Yr++u9LKnyypgFdIvTMg1VVKZhCOwAAFRXFdgAAAHhddESw7r3LeXX7RzuydOpCbjE9KoaQkBBJUmio85fMu3fvliT17NnTpU+zZs1Uu3ZtXbhwQVu3bnW0Hzt2TAcPHlTlypU1cOBAl34jRoyQ9N212X+o4LrsBdtvtk+7du3UuHFjHThwQMePH3fZDgAA8EODCq1u33XwWoXPiPCtRWsz9PclaS7tw3pGauqYKgoOotAOAEBFRrEdAAAAPjGyT5TCQn+wuj1fWrQuw4cj8q20tDTH6u/CxfGsrCxJUlxcXJF9q1atKkn64osvHG0F91u2bOko4v9Qu3btJEn79+93tKWnp+vEiROSpLZt27r0qVevnqpVq6aUlBRdvvz9mQgKXqtgn2V5LQAAgKL0/Em4Iit/nxGNkVaxuh0+YIzRmx+k640Vrv+PMrZ/tH41Mk5BFNoBAKjwKLYDAADAJ2Kjgl1WLq3bnqUzFyvGyqXk5GQ9+OCDGj9+vPr376/69etr586dmjBhgsaOHev03Pj4eElSSkqKy36MMY72H24vKJrXrVu3yNcvaC+qT1xcnCIiItzu585rFeX69eu6fPmy0w0AAFQsYaFB6tfROYes3Z6pnFxTTA/A84wx+seSNL1dxKWuHh4co4eHxMpmo9AOAAAotgMAAMCH7usbrdCQ77+kysuX/t/6ilFgPXv2rBYsWKCFCxdq/fr1yszM1OTJk/Xiiy8qKMg5pvfo0UOStGDBApf9vPfee8rI+G61zZUrVxztmZnfrQALDw8v8vULiunu9Pm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      "text/plain": [
       "<Figure size 2000x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20, 10), constrained_layout = True)\n",
    "\n",
    "nrwos = 2\n",
    "ncols = 3\n",
    "\n",
    "TickSize = 15\n",
    "fontsizeValue = 25\n",
    "TitleSizeValue = 20\n",
    "\n",
    "x = np.arange(0, 1.05, 0.05)\n",
    "\n",
    "sub = fig.add_subplot(nrwos, ncols, 1)\n",
    "sub.set_title('A. Preferential attachment', fontsize = TitleSizeValue)\n",
    "sub.plot(x, resPA, lw = 3, color='royalblue')\n",
    "xmax = np.argmax(resPA)\n",
    "sub.plot(x[xmax], resPA[xmax], mec='k', mew=3, ms=15, marker = 'o', color='royalblue')\n",
    "sub.text(x[xmax]-0.1, resPA[xmax]-0.1, r'$\\theta = 0.37$', fontsize=fontsizeValue-5)\n",
    "#sub.set_xlabel(r\"$\\theta$\", fontsize=fontsizeValue)\n",
    "sub.set_ylabel(\"log likelihood\", fontsize=fontsizeValue)\n",
    "sub.ticklabel_format(useOffset=False)\n",
    "\n",
    "sub.xaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.yaxis.set_tick_params(labelsize=TickSize)\n",
    "\n",
    "sub = fig.add_subplot(nrwos, ncols, 2)\n",
    "sub.set_title('B. Transitivity', fontsize = TitleSizeValue)\n",
    "sub.plot(x, resTransitivity, lw = 3, color='royalblue')\n",
    "xmax = np.argmax(resTransitivity)\n",
    "sub.plot(x[xmax], resTransitivity[xmax], mec='k', mew=3, ms=15, marker = 'o', color='royalblue')\n",
    "sub.text(x[xmax]-0.2, resTransitivity[xmax]-2, r'$\\theta = 0.889$', fontsize=fontsizeValue-5)\n",
    "#sub.set_xlabel(r\"$\\theta$\", fontsize=fontsizeValue)\n",
    "#sub.set_ylabel(\"log likelihood\", fontsize=fontsizeValue)\n",
    "\n",
    "sub.xaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.yaxis.set_tick_params(labelsize=TickSize)\n",
    "\n",
    "sub = fig.add_subplot(nrwos, ncols, 3)\n",
    "sub.set_title('E. Opinion selectivity', fontsize = TitleSizeValue)\n",
    "sub.plot(x, resOpinionSelectivity, lw = 3, color='royalblue')\n",
    "xmax = np.argmax(resOpinionSelectivity)\n",
    "sub.plot(x[xmax], resOpinionSelectivity[xmax], mec='k', mew=3, ms=15, marker = 'o', color='royalblue')\n",
    "sub.text(x[xmax]-0.1, resOpinionSelectivity[xmax]-100, r'$\\theta = 0.165$', fontsize=fontsizeValue-5)\n",
    "#sub.set_xlabel(r\"$\\theta$\", fontsize=fontsizeValue)\n",
    "#sub.set_ylabel(\"log likelihood\", fontsize=fontsizeValue)\n",
    "\n",
    "sub.xaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.yaxis.set_tick_params(labelsize=TickSize)\n",
    "\n",
    "sub = fig.add_subplot(nrwos, ncols, 4)\n",
    "sub.set_title('F. Age selectivity', fontsize = TitleSizeValue)\n",
    "sub.plot(x, resAgeSelectivity, lw = 3, color='royalblue')\n",
    "xmax = np.argmax(resAgeSelectivity)\n",
    "sub.plot(x[xmax], resAgeSelectivity[xmax], mec='k', mew=3, ms=15, marker = 'o', color='royalblue')\n",
    "sub.text(x[xmax], resAgeSelectivity[xmax]-200, r'$\\theta = 0$', fontsize=fontsizeValue-5)\n",
    "sub.set_xlabel(r\"$\\theta$\", fontsize=fontsizeValue)\n",
    "sub.set_ylabel(\"log likelihood\", fontsize=fontsizeValue)\n",
    "\n",
    "sub.xaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.yaxis.set_tick_params(labelsize=TickSize)\n",
    "\n",
    "sub = fig.add_subplot(nrwos, ncols, 5)\n",
    "sub.set_title('G. Gender selectivity', fontsize = TitleSizeValue)\n",
    "sub.plot(x, resGenderSelectivity, lw = 3, color='royalblue')\n",
    "xmax = np.argmax(resGenderSelectivity)\n",
    "sub.plot(x[xmax], resGenderSelectivity[xmax], mec='k', mew=3, ms=15, marker = 'o', color='royalblue')\n",
    "sub.text(x[xmax]-0.1, resGenderSelectivity[xmax]-200, r'$\\theta = 0.202$', fontsize=fontsizeValue-5)\n",
    "sub.set_xlabel(r\"$\\theta$\", fontsize=fontsizeValue)\n",
    "#sub.set_ylabel(\"log likelihood\", fontsize=fontsizeValue)\n",
    "\n",
    "sub.xaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.yaxis.set_tick_params(labelsize=TickSize)\n",
    "\n",
    "sub = fig.add_subplot(nrwos, ncols, 6)\n",
    "sub.set_title('D. Age activity', fontsize = TitleSizeValue)\n",
    "sub.plot(x, resAgeActivity, lw = 3, color='royalblue')\n",
    "xmax = np.argmax(resAgeActivity)\n",
    "sub.plot(x[xmax], resAgeActivity[xmax], mec='k', mew=3, ms=15, marker = 'o', color='royalblue')\n",
    "sub.text(x[xmax]-0.2, resAgeActivity[xmax]-100, r'$\\theta = 1$', fontsize=fontsizeValue-5)\n",
    "sub.set_xlabel(r\"$\\theta$\", fontsize=fontsizeValue)\n",
    "#sub.set_ylabel(\"log likelihood\", fontsize=fontsizeValue)\n",
    "sub.ticklabel_format(useOffset=False)\n",
    "\n",
    "sub.xaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.yaxis.set_tick_params(labelsize=TickSize)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3b7742e-30c7-4c1c-b7d1-26f63752e1cd",
   "metadata": {},
   "source": [
    "# MLE analysis (Table 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e162474a-5882-4a44-af85-f0e8dde601bb",
   "metadata": {},
   "source": [
    "### Null model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8bc4ce25-469e-4148-a7c5-d6e89a64b2e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-37711.765\n"
     ]
    }
   ],
   "source": [
    "Distr = [PA_DistrSucc]\n",
    "theta = [0]\n",
    "\n",
    "print(np.round(LikelihoodFunction(theta, Distr, N_DeletedTies, N_ConnectedPairs), 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb695648-6ff7-456b-bc1b-116e338dee86",
   "metadata": {},
   "source": [
    "### PA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8c64b8ab-5af4-4f1d-8f91-13d580cc6fb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obj function: 37711.59649070156\n",
      "Obj function (rounded): 37712.0\n",
      "\n",
      "Estimated theta: [0.37017045]\n",
      "Estimated theta (rounded): [0.37]\n"
     ]
    }
   ],
   "source": [
    "Distr = [PA_DistrSucc]\n",
    "\n",
    "ThetaGrid = np.arange(0, 1.05, 0.05)\n",
    "\n",
    "ObjFunctionValues = []\n",
    "Coefficients = []\n",
    "\n",
    "for x01 in ThetaGrid:\n",
    "    \n",
    "    x0 = np.array([x01])\n",
    "    \n",
    "    print(x0)\n",
    "    clear_output(wait=True)\n",
    "\n",
    "    Bounds = ((0, 1),)\n",
    "\n",
    "    Constraints = (\n",
    "                   {'type': 'ineq', 'fun': lambda x: 1 - x.sum()},\n",
    "                  )\n",
    "\n",
    "    res = minimize(ObjFunction, \n",
    "                   x0, \n",
    "                   args=(Distr, N_DeletedTies, N_ConnectedPairs, ), \n",
    "                   bounds=Bounds,\n",
    "                   constraints=Constraints, method='trust-constr')\n",
    "    \n",
    "    ObjFunctionValues.append(res.fun)\n",
    "    Coefficients.append(res.x)\n",
    "\n",
    "ObjFunctionValues = np.array(ObjFunctionValues)\n",
    "\n",
    "index = np.argmin(ObjFunctionValues)\n",
    "\n",
    "print(f'Obj function: {ObjFunctionValues[index]}')\n",
    "print(f'Obj function (rounded): {np.round(ObjFunctionValues[index], 0)}')\n",
    "print('')\n",
    "print(f'Estimated theta: {Coefficients[index]}')\n",
    "print(f'Estimated theta (rounded): {np.round(Coefficients[index], 3)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49d9b8b8-0529-48bb-8f87-5e84d21103e5",
   "metadata": {},
   "source": [
    "### TrCl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cfbc9415-e2ad-41c6-b48b-089215e2ff39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obj function: 37700.8137161731\n",
      "Obj function (rounded): 37701.0\n",
      "\n",
      "Estimated theta: [0.88891462]\n",
      "Estimated theta (rounded): [0.889]\n"
     ]
    }
   ],
   "source": [
    "Distr = [TransitivityDistrSucc]\n",
    "\n",
    "ThetaGrid = np.arange(0, 1.05, 0.05)\n",
    "\n",
    "ObjFunctionValues = []\n",
    "Coefficients = []\n",
    "\n",
    "for x01 in ThetaGrid:\n",
    "    \n",
    "    x0 = np.array([x01])\n",
    "    \n",
    "    print(x0)\n",
    "    clear_output(wait=True)\n",
    "\n",
    "    Bounds = ((0, 1),)\n",
    "\n",
    "    Constraints = (\n",
    "                   {'type': 'ineq', 'fun': lambda x: 1 - x.sum()},\n",
    "                  )\n",
    "\n",
    "    res = minimize(ObjFunction, \n",
    "                   x0, \n",
    "                   args=(Distr, N_DeletedTies, N_ConnectedPairs, ), \n",
    "                   bounds=Bounds,\n",
    "                   constraints=Constraints, method='trust-constr')\n",
    "    \n",
    "    ObjFunctionValues.append(res.fun)\n",
    "    Coefficients.append(res.x)\n",
    "\n",
    "ObjFunctionValues = np.array(ObjFunctionValues)\n",
    "\n",
    "index = np.argmin(ObjFunctionValues)\n",
    "\n",
    "print(f'Obj function: {ObjFunctionValues[index]}')\n",
    "print(f'Obj function (rounded): {np.round(ObjFunctionValues[index], 0)}')\n",
    "print('')\n",
    "print(f'Estimated theta: {Coefficients[index]}')\n",
    "print(f'Estimated theta (rounded): {np.round(Coefficients[index], 3)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6ab132a-29e5-44e5-b09f-8e0509872d35",
   "metadata": {},
   "source": [
    "### Opinion selectivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0abdd69c-0db1-4273-b337-d745133b7c5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obj function: 37688.133175665556\n",
      "Obj function (rounded): 37688.0\n",
      "\n",
      "Estimated theta: [0.16485068]\n",
      "Estimated theta (rounded): [0.165]\n"
     ]
    }
   ],
   "source": [
    "Distr = [OpinionSelectivityDistrSucc, ]\n",
    "\n",
    "ThetaGrid = np.arange(0, 1.05, 0.05)\n",
    "\n",
    "ObjFunctionValues = []\n",
    "Coefficients = []\n",
    "\n",
    "for x01 in ThetaGrid:\n",
    "    \n",
    "    x0 = np.array([x01])\n",
    "    \n",
    "    print(x0)\n",
    "    clear_output(wait=True)\n",
    "\n",
    "    Bounds = ((0, 1),)\n",
    "\n",
    "    Constraints = (\n",
    "                   {'type': 'ineq', 'fun': lambda x: 1 - x.sum()},\n",
    "                  )\n",
    "\n",
    "    res = minimize(ObjFunction, \n",
    "                   x0, \n",
    "                   args=(Distr, N_DeletedTies, N_ConnectedPairs, ), \n",
    "                   bounds=Bounds,\n",
    "                   constraints=Constraints, method='trust-constr')\n",
    "    \n",
    "    ObjFunctionValues.append(res.fun)\n",
    "    Coefficients.append(res.x)\n",
    "\n",
    "ObjFunctionValues = np.array(ObjFunctionValues)\n",
    "\n",
    "index = np.argmin(ObjFunctionValues)\n",
    "\n",
    "print(f'Obj function: {ObjFunctionValues[index]}')\n",
    "print(f'Obj function (rounded): {np.round(ObjFunctionValues[index], 0)}')\n",
    "print('')\n",
    "print(f'Estimated theta: {Coefficients[index]}')\n",
    "print(f'Estimated theta (rounded): {np.round(Coefficients[index], 3)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30a7c682-b269-46a5-9a69-bc61c7e5a244",
   "metadata": {},
   "source": [
    "### Age selectivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "61dc33df-274b-48f1-8ffa-e4057e5aff1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obj function: 37711.765828689226\n",
      "Obj function (rounded): 37712.0\n",
      "\n",
      "Estimated theta: [1.74921096e-05]\n",
      "Estimated theta (rounded): [0.]\n"
     ]
    }
   ],
   "source": [
    "Distr = [AgeSelectivityDistrSucc, ]\n",
    "\n",
    "ThetaGrid = np.arange(0, 1.05, 0.05)\n",
    "\n",
    "ObjFunctionValues = []\n",
    "Coefficients = []\n",
    "\n",
    "for x01 in ThetaGrid:\n",
    "    \n",
    "    x0 = np.array([x01])\n",
    "    \n",
    "    print(x0)\n",
    "    clear_output(wait=True)\n",
    "\n",
    "    Bounds = ((0, 1),)\n",
    "\n",
    "    Constraints = (\n",
    "                   {'type': 'ineq', 'fun': lambda x: 1 - x.sum()},\n",
    "                  )\n",
    "    \n",
    "    try:\n",
    "    \n",
    "        res = minimize(ObjFunction, \n",
    "                       x0, \n",
    "                       args=(Distr, N_DeletedTies, N_ConnectedPairs, ), \n",
    "                       bounds=Bounds,\n",
    "                       constraints=Constraints, method='trust-constr')\n",
    "\n",
    "        ObjFunctionValues.append(res.fun)\n",
    "        Coefficients.append(res.x)\n",
    "        \n",
    "    except Exception as exception:\n",
    "        \n",
    "        pass\n",
    "\n",
    "ObjFunctionValues = np.array(ObjFunctionValues)\n",
    "\n",
    "index = np.argmin(ObjFunctionValues)\n",
    "\n",
    "print(f'Obj function: {ObjFunctionValues[index]}')\n",
    "print(f'Obj function (rounded): {np.round(ObjFunctionValues[index], 0)}')\n",
    "print('')\n",
    "print(f'Estimated theta: {Coefficients[index]}')\n",
    "print(f'Estimated theta (rounded): {np.round(Coefficients[index], 3)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d79e111c-fc29-4c68-8af8-444caadd53bf",
   "metadata": {},
   "source": [
    "### Gender selectivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "752bdd08-7db3-49dd-9022-fac7b35d1e6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obj function: 37636.03910556818\n",
      "Obj function (rounded): 37636.0\n",
      "\n",
      "Estimated theta: [0.20187225]\n",
      "Estimated theta (rounded): [0.202]\n"
     ]
    }
   ],
   "source": [
    "Distr = [GenderSelectivityDistrSucc, ]\n",
    "\n",
    "ThetaGrid = np.arange(0, 1.05, 0.05)\n",
    "\n",
    "ObjFunctionValues = []\n",
    "Coefficients = []\n",
    "\n",
    "for x01 in ThetaGrid:\n",
    "    \n",
    "    x0 = np.array([x01])\n",
    "    \n",
    "    print(x0)\n",
    "    clear_output(wait=True)\n",
    "\n",
    "    Bounds = ((0, 1),)\n",
    "\n",
    "    Constraints = (\n",
    "                   {'type': 'ineq', 'fun': lambda x: 1 - x.sum()},\n",
    "                  )\n",
    "\n",
    "    res = minimize(ObjFunction, \n",
    "                   x0, \n",
    "                   args=(Distr, N_DeletedTies, N_ConnectedPairs, ), \n",
    "                   bounds=Bounds,\n",
    "                   constraints=Constraints, method='trust-constr')\n",
    "    \n",
    "    ObjFunctionValues.append(res.fun)\n",
    "    Coefficients.append(res.x)\n",
    "\n",
    "ObjFunctionValues = np.array(ObjFunctionValues)\n",
    "\n",
    "index = np.argmin(ObjFunctionValues)\n",
    "\n",
    "print(f'Obj function: {ObjFunctionValues[index]}')\n",
    "print(f'Obj function (rounded): {np.round(ObjFunctionValues[index], 0)}')\n",
    "print('')\n",
    "print(f'Estimated theta: {Coefficients[index]}')\n",
    "print(f'Estimated theta (rounded): {np.round(Coefficients[index], 3)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69edde77-7cf0-47f2-8712-c530557cd8dd",
   "metadata": {},
   "source": [
    "### Age-based activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "96e1bd3f-3830-4d7e-9c0e-1ce6fe344e9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obj function: 37509.81249378991\n",
      "Obj function (rounded): 37510.0\n",
      "\n",
      "Estimated theta: [0.9999357]\n",
      "Estimated theta (rounded): [1.]\n"
     ]
    }
   ],
   "source": [
    "Distr = [ActivityDistrSucc, ]\n",
    "\n",
    "ThetaGrid = np.arange(0, 1.05, 0.05)\n",
    "\n",
    "ObjFunctionValues = []\n",
    "Coefficients = []\n",
    "\n",
    "for x01 in ThetaGrid:\n",
    "    \n",
    "    x0 = np.array([x01])\n",
    "    \n",
    "    print(x0)\n",
    "    clear_output(wait=True)\n",
    "\n",
    "    Bounds = ((0, 1),)\n",
    "\n",
    "    Constraints = (\n",
    "                   {'type': 'ineq', 'fun': lambda x: 1 - x.sum()},\n",
    "                  )\n",
    "\n",
    "    res = minimize(ObjFunction, \n",
    "                   x0, \n",
    "                   args=(Distr, N_DeletedTies, N_ConnectedPairs, ), \n",
    "                   bounds=Bounds,\n",
    "                   constraints=Constraints, method='trust-constr')\n",
    "    \n",
    "    ObjFunctionValues.append(res.fun)\n",
    "    Coefficients.append(res.x)\n",
    "\n",
    "ObjFunctionValues = np.array(ObjFunctionValues)\n",
    "\n",
    "index = np.argmin(ObjFunctionValues)\n",
    "\n",
    "print(f'Obj function: {ObjFunctionValues[index]}')\n",
    "print(f'Obj function (rounded): {np.round(ObjFunctionValues[index], 0)}')\n",
    "print('')\n",
    "print(f'Estimated theta: {Coefficients[index]}')\n",
    "print(f'Estimated theta (rounded): {np.round(Coefficients[index], 3)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17deeb47-8ab7-4f4a-895c-df2f43f08e15",
   "metadata": {},
   "source": [
    "# Multivariate analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b55770c-2fcd-479f-abdc-a07eaecfa854",
   "metadata": {},
   "source": [
    "### PA + Triadic closure + Opinion selectivity + Age selectivity + Gender selectivituy + Age-based activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "830b8083-12b6-4425-a718-f4a2998ca923",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obj function: 37466.08732986097\n",
      "Obj function (rounded): 37466.0\n",
      "\n",
      "Estimated theta: [2.48886454e-10 1.44940279e-09 3.74794261e-02 2.11857764e-09\n",
      " 1.49405387e-01 8.13115182e-01]\n",
      "Estimated theta (rounded): [0.    0.    0.037 0.    0.149 0.813]\n"
     ]
    }
   ],
   "source": [
    "Distr = [PA_DistrSucc, TransitivityDistrSucc, \n",
    "         OpinionSelectivityDistrSucc, AgeSelectivityDistrSucc, GenderSelectivityDistrSucc, \n",
    "         ActivityDistrSucc]\n",
    "\n",
    "ThetaGrid = np.arange(0, 1.2, 0.2)\n",
    "\n",
    "ObjFunctionValues = []\n",
    "Coefficients = []\n",
    "\n",
    "for x01 in ThetaGrid:\n",
    "    for x02 in ThetaGrid:\n",
    "        if x01+x02 > 1:\n",
    "            break\n",
    "        else:\n",
    "            for x03 in ThetaGrid:\n",
    "                if x01+x02+x03 > 1:\n",
    "                    break\n",
    "                else:\n",
    "                    for x04 in ThetaGrid:\n",
    "                        if x01+x02+x03+x04 > 1:\n",
    "                            break\n",
    "                        else:\n",
    "                            for x05 in ThetaGrid:\n",
    "                                if x01+x02+x03+x04+x05 > 1:\n",
    "                                    break\n",
    "                                else:\n",
    "                                    for x06 in ThetaGrid:\n",
    "                                        if x01+x02+x03+x04+x05+x06 > 1:\n",
    "                                            break\n",
    "                                        else:\n",
    "                                            x0 = np.array([x01, x02, x03, x04, x05, x06])\n",
    "                                            \n",
    "                                            print(x0)\n",
    "                                            clear_output(wait=True)\n",
    "                                            \n",
    "                                            Bounds = ((0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1))\n",
    "\n",
    "                                            Constraints = (\n",
    "                                                           {'type': 'ineq', 'fun': lambda x: 1 - x.sum()}, \n",
    "                                                          )\n",
    "                                            #if 1 == 1:\n",
    "                                            try:\n",
    "\n",
    "                                                res = minimize(ObjFunction, \n",
    "                                                               x0, \n",
    "                                                               args=(Distr, N_DeletedTies, N_ConnectedPairs, ), \n",
    "                                                               bounds=Bounds,\n",
    "                                                               constraints=Constraints, method='trust-constr')\n",
    "\n",
    "                                                ObjFunctionValues.append(res.fun)\n",
    "                                                Coefficients.append(res.x)\n",
    "\n",
    "                                            except Exception as exception:\n",
    "                                            #else:\n",
    "                                                pass\n",
    "\n",
    "ObjFunctionValues = np.array(ObjFunctionValues)\n",
    "\n",
    "index = np.argmin(ObjFunctionValues)\n",
    "\n",
    "print(f'Obj function: {ObjFunctionValues[index]}')\n",
    "print(f'Obj function (rounded): {np.round(ObjFunctionValues[index], 0)}')\n",
    "print('')\n",
    "print(f'Estimated theta: {Coefficients[index]}')\n",
    "print(f'Estimated theta (rounded): {np.round(Coefficients[index], 3)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fce2e5e-c294-4dd3-abd7-da8a9ee790db",
   "metadata": {},
   "source": [
    "# Multivariate MLE + control for degree factor\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a17efda-dbc1-4782-944e-d813c8e47ac3",
   "metadata": {},
   "source": [
    "### Choose the degree control mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "093d0276-028a-47c4-a254-a6e076e0bb6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Mask_Degree_Control = np.load(f'{folder}/Masks/MaskFewFew.npy')\n",
    "#Mask_Degree_Control = np.load(f'{folder}/Masks/MaskFewAvg.npy')\n",
    "#Mask_Degree_Control = np.load(f'{folder}/Masks/MaskFewMany.npy')\n",
    "#Mask_Degree_Control = np.load(f'{folder}/Masks/MaskAvgAvg.npy')\n",
    "#Mask_Degree_Control = np.load(f'{folder}/Masks/MaskAvgMany.npy')\n",
    "Mask_Degree_Control = np.load(f'{folder}/Masks/MaskManyMany.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "46780144-7c35-4a89-b698-8ded087057d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of observations (connected pairs): 85317\n",
      "\n",
      "Number of successes (connected pairs that have become disjointed): 1159\n"
     ]
    }
   ],
   "source": [
    "Target = np.load(f'{folder}/Masks/Target.npy')\n",
    "\n",
    "\n",
    "MaskDeleteTie = (Target == 3)\n",
    "MaskWereFriends = (Target == 3) | (Target == 4)\n",
    "\n",
    "MaskStaticOpinions = np.load(f'{folder}/Masks/MaskStaticOpinions.npy')\n",
    "\n",
    "FriendsBeforeNumI = np.load(f'{folder}/Vectors/FriendsBeforeNumI.npy')\n",
    "FriendsBeforeNumJ = np.load(f'{folder}/Vectors/FriendsBeforeNumJ.npy')\n",
    "\n",
    "CommFriendsBefore = np.load(f'{folder}/Vectors/CommFriendsBefore.npy')\n",
    "\n",
    "OpinionBeforeI = np.load(f'{folder}/Vectors/OpinionBeforeI.npy')\n",
    "OpinionBeforeJ = np.load(f'{folder}/Vectors/OpinionBeforeJ.npy')\n",
    "\n",
    "ageI = np.load(f'{folder}/Vectors/ageI.npy')\n",
    "ageJ = np.load(f'{folder}/Vectors/ageJ.npy')\n",
    "\n",
    "sexI = np.load(f'{folder}/Vectors/sexI.npy')\n",
    "sexJ = np.load(f'{folder}/Vectors/sexJ.npy')\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "FriendsBeforeNumI = FriendsBeforeNumI[MaskWereFriends*MaskStaticOpinions*Mask_Degree_Control]\n",
    "FriendsBeforeNumJ = FriendsBeforeNumJ[MaskWereFriends*MaskStaticOpinions*Mask_Degree_Control]\n",
    "\n",
    "MaskDeleteTie = MaskDeleteTie[MaskWereFriends*MaskStaticOpinions*Mask_Degree_Control]\n",
    "\n",
    "CommFriendsBefore = CommFriendsBefore[MaskWereFriends*MaskStaticOpinions*Mask_Degree_Control]\n",
    "\n",
    "OpinionBeforeI = OpinionBeforeI[MaskWereFriends*MaskStaticOpinions*Mask_Degree_Control]\n",
    "OpinionBeforeJ = OpinionBeforeJ[MaskWereFriends*MaskStaticOpinions*Mask_Degree_Control]\n",
    "\n",
    "ageI = ageI[MaskWereFriends*MaskStaticOpinions*Mask_Degree_Control]\n",
    "ageJ = ageJ[MaskWereFriends*MaskStaticOpinions*Mask_Degree_Control]\n",
    "\n",
    "sexI = sexI[MaskWereFriends*MaskStaticOpinions*Mask_Degree_Control]\n",
    "sexJ = sexJ[MaskWereFriends*MaskStaticOpinions*Mask_Degree_Control]\n",
    "\n",
    "del(MaskWereFriends, MaskStaticOpinions)\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "FriendsBeforeProduct = FriendsBeforeNumI * FriendsBeforeNumJ\n",
    "\n",
    "del(FriendsBeforeNumI, FriendsBeforeNumJ,)\n",
    "\n",
    "PA_Distr = FriendsBeforeProduct.max() - FriendsBeforeProduct + 1\n",
    "\n",
    "PA_Distr = PA_Distr / PA_Distr.sum()\n",
    "\n",
    "del(FriendsBeforeProduct)\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "TransitivityDistr = CommFriendsBefore.max() - CommFriendsBefore + 0.1\n",
    "\n",
    "TransitivityDistr = TransitivityDistr / TransitivityDistr.sum()\n",
    "\n",
    "del(CommFriendsBefore)\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "OpinionDiffBefore = abs(OpinionBeforeI - OpinionBeforeJ)\n",
    "\n",
    "OpinionSelectivityDistr = OpinionDiffBefore + 0.01\n",
    "\n",
    "del(OpinionBeforeI, OpinionBeforeJ, OpinionDiffBefore)\n",
    "\n",
    "OpinionSelectivityDistr = OpinionSelectivityDistr / OpinionSelectivityDistr.sum()\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "ageDiff = abs(ageI - ageJ)\n",
    "\n",
    "AgeSelectivityDistr = ageDiff + 1\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "AgeProduct = ageI * ageJ\n",
    "\n",
    "ActivityDistr = AgeProduct.max() - AgeProduct + 1\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "del(ageI, ageJ, ageDiff)\n",
    "\n",
    "ActivityDistr = ActivityDistr / ActivityDistr.sum()\n",
    "\n",
    "AgeSelectivityDistr = AgeSelectivityDistr / AgeSelectivityDistr.sum()\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "sexDiff = abs(sexI - sexJ)\n",
    "\n",
    "GenderSelectivityDistr = sexDiff + 0.1\n",
    "\n",
    "del(sexI, sexJ, sexDiff)\n",
    "\n",
    "GenderSelectivityDistr = GenderSelectivityDistr / GenderSelectivityDistr.sum()\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "PA_DistrSucc = PA_Distr[MaskDeleteTie]\n",
    "TransitivityDistrSucc = TransitivityDistr[MaskDeleteTie]\n",
    "OpinionSelectivityDistrSucc = OpinionSelectivityDistr[MaskDeleteTie]\n",
    "AgeSelectivityDistrSucc = AgeSelectivityDistr[MaskDeleteTie]\n",
    "GenderSelectivityDistrSucc = GenderSelectivityDistr[MaskDeleteTie]\n",
    "\n",
    "ActivityDistrSucc = ActivityDistr[MaskDeleteTie]\n",
    "\n",
    "N_ConnectedPairs = len(PA_Distr)\n",
    "N_DeletedTies = len(PA_DistrSucc)\n",
    "\n",
    "del(TransitivityDistr, \n",
    "    PA_Distr, \n",
    "    MaskDeleteTie, OpinionSelectivityDistr, AgeSelectivityDistr, GenderSelectivityDistr, ActivityDistr)\n",
    "\n",
    "print(f'Number of observations (connected pairs): {N_ConnectedPairs}')\n",
    "\n",
    "print('')\n",
    "\n",
    "print(f'Number of successes (connected pairs that have become disjointed): {N_DeletedTies}')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfea0569-cd62-4f32-bbce-c735c292fc51",
   "metadata": {},
   "source": [
    "### Triadic closure + Opinion selectivity + Age selectivity + Gender selectivity + Age-based activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b74fe75f-deb4-43a5-bba2-8210eb3d2773",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obj function: 13109.200306062256\n",
      "Obj function (rounded): 13109.0\n",
      "\n",
      "Estimated theta: [5.43728120e-09 2.93121670e-02 1.09715008e-01 1.08361882e-01\n",
      " 7.52610930e-01]\n",
      "Estimated theta (rounded): [0.    0.029 0.11  0.108 0.753]\n"
     ]
    }
   ],
   "source": [
    "Distr = [TransitivityDistrSucc, \n",
    "         OpinionSelectivityDistrSucc, AgeSelectivityDistrSucc, GenderSelectivityDistrSucc, \n",
    "         ActivityDistrSucc]\n",
    "\n",
    "ThetaGrid = np.arange(0, 1.2, 0.2)\n",
    "\n",
    "ObjFunctionValues = []\n",
    "Coefficients = []\n",
    "\n",
    "for x01 in ThetaGrid:\n",
    "    for x02 in ThetaGrid:\n",
    "        if x01+x02 > 1:\n",
    "            break\n",
    "        else:\n",
    "            for x03 in ThetaGrid:\n",
    "                if x01+x02+x03 > 1:\n",
    "                    break\n",
    "                else:\n",
    "                    for x04 in ThetaGrid:\n",
    "                        if x01+x02+x03+x04 > 1:\n",
    "                            break\n",
    "                        else:\n",
    "                            for x05 in ThetaGrid:\n",
    "                                if x01+x02+x03+x04+x05 > 1:\n",
    "                                    break\n",
    "                                else:\n",
    "                                    x0 = np.array([x01, x02, x03, x04, x05])\n",
    "                                    print(x0)\n",
    "                                    clear_output(wait=True)\n",
    "                                            \n",
    "                                    Bounds = ((0, 1), (0, 1), (0, 1), (0, 1), (0, 1))\n",
    "\n",
    "                                    Constraints = (\n",
    "                                                           {'type': 'ineq', 'fun': lambda x: 1 - x.sum()}, \n",
    "                                                          )\n",
    "                                            #if 1 == 1:\n",
    "                                    try:\n",
    "\n",
    "                                        res = minimize(ObjFunction, \n",
    "                                                               x0, \n",
    "                                                               args=(Distr, N_DeletedTies, N_ConnectedPairs, ), \n",
    "                                                               bounds=Bounds,\n",
    "                                                               constraints=Constraints, method='trust-constr')\n",
    "\n",
    "                                        ObjFunctionValues.append(res.fun)\n",
    "                                        Coefficients.append(res.x)\n",
    "\n",
    "                                    except Exception as exception:\n",
    "                                            #else:\n",
    "                                        pass\n",
    "\n",
    "ObjFunctionValues = np.array(ObjFunctionValues)\n",
    "\n",
    "index = np.argmin(ObjFunctionValues)\n",
    "\n",
    "print(f'Obj function: {ObjFunctionValues[index]}')\n",
    "print(f'Obj function (rounded): {np.round(ObjFunctionValues[index], 0)}')\n",
    "print('')\n",
    "print(f'Estimated theta: {Coefficients[index]}')\n",
    "print(f'Estimated theta (rounded): {np.round(Coefficients[index], 3)}')"
   ]
  }
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